Effect of Persistent Organic Pollutants on malaria in Chinese mainland: a population-based surveillance and modelling study

Effect of Persistent Organic Pollutants on malaria in Chinese mainland: a population-based surveillance and modelling study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of Persistent Organic Pollutants on malaria in Chinese mainland: a population-based surveillance and modelling study Guolong Qu, Jianqiang Han, Zhenyao Song, Weiming Hou This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8542332/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates malaria incidence trends in mainland China from 2005 to 2020, to elucidate its epidemiological characteristics and investigate potential associations with air pollution. First, time series analysis and machine learning methods were employed to predict malaria incidence. Next, the weighted quantile sum (WQS) model and distributed lag nonlinear model (DLNM) were utilized to assess the risk of malaria linked to persistent organic pollutants (POPs). Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model outperforms the Holt-Winters model in univariate traditional models for malaria, with the optimally configured SARIMA (1,1,0) (1,0,1) [12]. For the new Kalman filter model, showing good results across both overall malaria and individual subtypes (MAE ∈ [0.001, 0.016]). In multivariate prediction, the models with the best performance are Gradient Boosting (XGBoost) and Support Vector Machine (SVM). Risk levels for Polychlorinated Biphenyls (PCB) and Hexachlorobenzene (HCB) were coefficients (95% CI): -1.48 (-2.69, -0.27) and -1.39 (-2.57, -0.22), respectively. Cumulative effect of extremely low-level HCB during the first 3 and 4 months were 3.602 (1.103, 11.765) and 4.749 (1.11, 20.31), respectively, indicating an increased risk of malaria incidence. Our current study not only investigated the spatiotemporal surveillance and early warning systems for malaria incidence in mainland China but also elucidated the lagged exposure-response relationships and potential associations between organic pollutants and malaria occurrence. Strengthening POPs emission control activities during this period may help reduce the risk of seasonal malaria susceptibility. Malaria Persistent Organic Pollutants PCB DLNM Kalman filter Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Malaria is a mosquito-borne infectious parasitic disease prevalent in tropical and subtropical regions( 1 ). In 2021, nearly half of the global population faced the risk of malaria, with an estimated 247 million cases and 619,000 deaths worldwide( 2 , 3 ). Malaria incidence shows a seasonal trend, and time series models are the most commonly used cyclical models for disease prediction. Several studies have shown that the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model performs well in predicting malaria incidence across different countries( 4 , 5 ). However, existing research predominantly focuses on model self-comparisons, with limited systematic evaluation of different time series methodologies or the development of novel modeling approaches—representing a significant gap in the field. Environmental factors play a crucial role in the dynamics and distribution of malaria( 6 , 7 ). From a biological perspective, meteorological factors are intrinsically linked to malaria incidence through their influence on mosquito vectors and the development of Plasmodium within the vectors. Studies have shown that under colder environmental conditions, larger daily temperature fluctuations accelerate malaria incidence( 8 ). Additionally, research has confirmed that the increase in malaria incidence occurs after a 30-day interval following the simultaneous occurrence of high temperatures and rainfall( 9 ). Although a Singaporean survey revealed an inverse association between ground-level SO₂ and PM₂.₅ concentrations and mosquito-borne disease incidence - investigating chemical pollutants as risk factors for malaria (a naturally occurring zoonotic disease) - remains epidemiologically valuable( 10 ). This is reflected in the complex network of relationships between various environmental factors and diseases. Moreover, when modeling the impact of environmental factors on malaria cases, two key issues need special attention: non-linear risk effects and lag effects, which are seldom considered together in some studies. Persistent Organic Pollutants (POPs) have a long history of application in commercial and industrial sectors. Individuals can be exposed to them through daily activities, dietary intake, and drinking water( 11 ). The concentrations of PCBs in the Lanzhou section of the Yellow River, China, fluctuate within the range of 3.08 to 32.3 ng/g, dominated by fully chlorinated congeners, with industrial activities identified as the primary source( 12 ). Multi-regional studies conducted in southeastern and northeastern China have revealed that 66.7% of organochlorine pesticides (OCPs) exhibit a detection frequency exceeding 80%, indicating their ubiquity in the atmosphere. Notably, hexachlorobenzene (HCB) was found to have the highest concentration across all sampling sites( 13 ). However, most studies have focused on direct exposure pathways, such as the relationship between POPs exposure in internal environments (e.g., blood) and diseases, which has shown some correlation between POPs and disease( 14 ). However, these studies have overlooked the long-term exposure risks from external environments, thus neglecting the potential harms of indirect exposure. Although some studies have explored the carcinogenic and non-carcinogenic risks of POPs in the air, they have not concluded that they pose a health threat to humans( 15 ). Additionally, previous animal studies evaluated mice fed with polychlorinated biphenyls (PCBs) for 3 to 6 weeks and subsequently inoculated with Plasmodium berghei (murine malaria parasite). The results demonstrated a 20% reduction in the average survival time of the exposed mice, which preliminarily indicated that environmental chemical pollutants could impair host resistance. It was hypothesized that the assessment of immune parameters might serve as a sensitive indicator for the toxic effects of such pollutants( 16 ). Current evidence fails to establish a clear association between malaria (a parasitic disease) and POPs, making this research particularly timely. Accordingly, this study employs a systematic approach comprising: ( 1 ) a three-dimensional spatial-temporal distribution analysis of malaria incidence cases across China; ( 2 ) a comprehensive descriptive analysis of domestic POPs emission sources; and ( 3 ) a two-stage predictive framework integrating incidence risk forecasting for malaria strains with subsequent multi-stage effect analysis. 2. Materials and Methods 2.1 Data Source and mapping of malaria cases in different regions Data on all reported malaria cases in mainland China from 2005 to 2020 were obtained from the Public Health Science Data Center Website ( https://www.phsciencedata.cn/ ). POPs (such as Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthene (BkF), Indeno[1,2,3-cd]pyrene (IcdP), Polychlorinated Biphenyls (PCB), Polychlorinated Dibenzo-p-dioxins (PCDD), and Hexachlorobenzene (HCB) ) as well as Greenhouse Gases (GHGs) (including Methane (CH 4 ), Biogenic Carbon Dioxide (CO 2 bio), Nitrous Oxide (N 2 O) and Carbon Dioxide (CO 2 ) ) were sourced from the Emissions Database for Global Atmospheric Research (EDGAR) ( https://edgar.jrc.ec.europa.eu/ ). According to geographical conditions, China is divided into seven regions in the planning: North China (NC), Northeast China (NE), East China (EC), Central China (CC), South China (SC), Southwest China (SW), and Northwest China (NW). 2.2 Different fields and sources of POPs and GHGs This study summarizes the emissions of POPs and GHGs using the EDGAR database, explores and ranks the main sources of these pollutants and industry sectors, and further analyzes the causes of pollutant generation. 2.3 Features selection Spearman correlation analysis was performed to examine associations between various POPs, GHGs, and the incidence of different malaria types. Analysis prioritized relationships with organic pollutants. For malaria subtypes exhibiting no significant correlations with any POPs, subsequent analyses focused on significant factors identified in other malaria types, ultimately selecting consistently significant variables across multiple malaria types for in-depth investigation. 2.4 Prediction analysis In the univariate prediction, this study uses time series analysis models to explore the effect of time variables on the prediction of incidence rates for different types of malaria, thereby accurately monitoring the disease. The time series models selected include Holt-Winters, SARIMA, and Kalman filter models. SARIMA is a model that combines seasonal differences with the ARIMA model, effectively modeling time series data with cyclical patterns. SARIMA (p, d, q) (P, D, Q) s includes seven parameters, which can be divided into two groups: three non-seasonal parameters (p, d, q) and four seasonal parameters (P, D, Q) s. The Holt-Winters exponential smoothing model eliminates some random fluctuations, assigning different weights to data from each period to predict future trends( 17 ). The Holt-Winters exponential smoothing model and the SARIMA model have been more specifically introduced in previous studies( 18 ). Kalman filtering is an optimal recursive estimation algorithm that estimates the current system state by integrating prior state estimates with current observations, fundamentally relying on state-space equations and Bayesian recursive estimation principles. Therefore, Kalman filtering does not require stationarity or time-varying signals. Specific formulas and algorithms can be found in previous research( 19 ). For the univariate model prediction effectiveness, we compared the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Standard Error (SE) indicators. This model was implemented using the "forecast" and "FKF" packages in R software version 4.4.1. In the multivariate prediction, this study employs various machine learning algorithms to explore the predictive effects of POPs and GHGs on the incidence of different types of malaria. A 70% dataset was used as the training set for multi-model comparison, while the remaining 30% dataset was used as the validation set to test the optimal algorithm. The machine learning algorithms used include Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Linear Support Vector Machine (LSVM), Linear Model (LM), and Neural Network (NNET). Specific algorithms can be found in the relevant literature references( 20 ). Cross-validation was performed using the vfold_cv() function, followed by multi-model fitting and comparison after the cross-validation. For the multivariate model prediction effectiveness, we compared the MAE indicator. This model was implemented using the "tidyverse," "kernlab," "nnet," "bonsai," "lightgbm," and "xgboost" packages in R software version 4.4.1. 2.5 Risk analysis The Weighted Quantile Sum (WQS) regression is used to examine the individual contributions of POP mixtures. The WQS model is widely applied to analyze the effects of multiple chemical exposures on human health( 21 ). In the present study, the model was set to 5000 iterations, with significant organic compounds identified in the preliminary feature extraction step being used as the main effects. Two comparison models were also set up: Model 1 without confounding factors, and Model 2 with significant greenhouse gases as confounding factors. Based on an index that includes all persistent organic pollutants, WQS regression was performed in both positive and negative directions. The weight of each organic compound ranges from 0 to 1, with the sum of the weights equaling 1( 22 ). The WQS coefficient is an indicator of the association strength between the weighted quantile score and the outcome variable, essentially reflecting the "overall effect" of the mixture. The sign of the coefficient corresponds to the direction of the effect, while the absolute value reflects the strength of the effect. The study represents the risk coefficient by calculating the coefficients of individual pollutant factors. This model was implemented using the "gWQS" package in R software version 4.4.1. 2.6 Lagging effect We used Distributed Lag Nonlinear Model (DLNM) to study the impact of monthly exposure to each organic compound on malaria incidence, while controlling for time variables (month) and other air pollutants, such as greenhouse gases, as confounding factors. We modeled each organic pollutant using 1 to 6 knots and selected the best-fitting model using the Quasi-Akaike Information Criterion (Q-AIC), which was also used to calculate the optimal lag months( 23 , 24 ). In this study, the optimal lag period was 6 months( 24 ). Furthermore, we calculated the cumulative effect and relative effect by exploring the relationship between each organic compound at specific levels and specific lag months. For the cumulative effect of organic compounds across all lag months (6 months), specific percentiles were chosen at P5, P25, P75, and P95 levels. For the lag effects at each month and cumulative effects across the months (0–6 months), the specific percentiles were selected at P5 and P95 levels. In addition, sensitivity analysis was performed on this model to determine its parameters. The degrees of freedom for the lag variables were set to df = 3 based on previous literature and customary practices. This model was implemented using the "dlnm" package in R software version 4.4.1. 3. Results 3.1 Epidemiological characteristics of malaria As shown in Table 1 , the overall reported malaria cases exhibit a declining trend over the years, with two anomalous peaks of increased incidence occurring in 2006 and 2013. Additionally, there was a short-term increase in reported cases between 2014 and 2016. The seasonal characteristics reveal an epidemic pattern in the summer and autumn (approximately 77.35%), with the majority of cases occurring in the summer (about 41.03%). When comparing the reported cases of different types of malaria, relapsing malaria predominates (about 72.56%), followed by unclassified malaria (about 14.52%), and malignant malaria (about 12.92%). Relapsing malaria and unclassified malaria are mainly prevalent in the summer and autumn, while malignant malaria is more prevalent in the spring and summer. Table 1 The regional and temporal distribution of different types of malaria in mainland China from 2005 to 2020. Distributions Year Series Regions 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 Relapsing malaria Malignant malaria Unclassified malaria Total CC 3970 6970 6021 4197 2431 1479 633 448 537 515 453 500 368 436 498 153 23521 3466 2622 29609 Henan 2302 5080 4151 3041 1617 887 314 157 193 222 187 205 180 186 232 71 16830 1573 622 19025 Hubei 1518 1753 1769 1083 712 429 167 127 131 136 124 148 92 128 151 35 5881 871 1751 8503 Hunan 150 137 101 73 102 163 152 164 213 157 142 147 96 122 115 47 810 1022 249 2081 SW 14159 12568 7840 5161 3870 2891 1579 840 723 777 864 744 504 441 407 219 37124 11260 5203 53587 Yunnan 13144 11544 6442 3640 2731 2115 1194 636 426 444 516 331 236 167 150 133 32129 9049 2671 43849 Guizhou 361 466 961 1285 864 397 153 15 13 29 14 36 24 26 28 4 2979 166 1531 4676 Sichuan 495 430 279 161 185 312 197 158 244 273 292 339 212 219 197 73 1633 1798 635 4066 Chongqing 66 58 60 43 47 35 32 23 36 24 34 38 31 29 32 9 208 245 144 597 Xizang 93 70 98 32 43 32 3 8 4 7 8 0 1 0 0 0 175 2 222 399 NW 47 48 73 68 115 58 69 49 75 136 114 120 96 122 138 40 349 705 314 1368 Shaanxi 33 30 49 47 63 40 35 31 41 58 75 82 61 89 93 30 201 453 203 857 Gansu 6 11 7 13 43 10 21 7 17 68 28 23 24 19 22 4 107 176 40 323 Xinjiang 2 6 13 5 8 6 6 7 7 4 3 6 7 7 13 4 26 41 37 104 Ningxia 5 1 2 3 0 1 3 2 5 4 7 8 4 3 9 2 11 23 25 59 Qinghai 1 0 2 0 1 1 4 2 5 2 1 1 0 4 1 0 4 12 9 25 NE 28 38 36 36 55 48 74 63 72 69 102 78 83 86 82 23 175 559 239 973 Liaoning 19 19 26 17 35 28 46 43 45 39 65 54 53 58 52 16 116 406 93 615 Jilin 6 14 7 13 11 13 12 13 12 25 22 17 20 12 12 2 31 93 87 211 Heilongjiang 3 5 3 6 9 7 16 7 15 5 15 7 10 16 18 5 28 60 59 147 EC 16789 36420 29047 14800 6729 2587 1396 653 977 996 1073 1094 925 853 880 319 92036 7553 15949 115538 Anhui 15693 34984 27307 13484 5918 1860 644 99 194 153 131 146 99 102 102 34 85877 993 14080 100950 Jiangsu 573 643 754 558 341 362 372 198 344 358 402 306 238 238 238 89 2682 2611 721 6014 Zhejiang 246 404 594 435 220 121 115 139 187 203 166 208 178 125 135 54 1936 1360 234 3530 Shandong 124 137 164 165 111 112 118 93 130 153 215 247 197 216 228 71 606 1387 488 2481 Fujian 46 52 28 23 65 67 68 54 51 63 77 114 144 109 113 42 301 685 130 1116 Shanghai 85 158 170 111 58 36 38 17 22 22 25 24 34 21 19 14 448 219 187 854 Jiangxi 22 42 30 24 16 29 41 53 49 44 57 49 35 42 45 15 186 298 109 593 NC 46 81 70 76 81 93 117 80 137 119 147 153 164 154 164 46 264 1033 431 1728 Hebei 19 26 23 29 39 41 50 35 54 55 60 89 84 77 91 28 99 519 182 800 Beijing 19 38 37 36 28 36 37 30 42 37 46 32 47 45 38 9 88 324 145 557 Shanxi 4 6 3 5 7 5 9 2 19 13 18 15 20 18 11 6 39 81 41 161 Tianjin 3 8 6 4 3 9 14 9 13 12 17 11 7 7 15 3 29 74 38 141 Neimenggu 1 3 1 2 4 2 7 4 9 2 6 6 6 7 9 0 9 35 25 69 SC 4617 4069 3587 2022 819 233 221 318 1376 309 363 458 539 405 340 223 8118 4191 7590 19899 Hainan 4313 3853 3387 1844 685 73 9 13 15 12 15 8 9 9 2 3 6918 794 6538 14250 Guangxi 152 95 76 57 51 65 116 218 1255 183 240 304 387 250 183 69 641 2398 662 3701 Guangdong 152 121 124 121 83 95 96 87 106 114 108 146 143 146 155 151 559 999 390 1948 Seasons Spring 6212 6131 5758 4077 2479 1619 1077 619 927 714 864 878 699 591 522 171 20367 7802 5169 33338 Summer 15533 21182 24230 11727 6902 2955 1413 758 1698 927 921 789 817 654 682 182 69981 8916 12473 91370 Autumn 15195 30328 14481 9330 3739 1973 890 528 600 629 642 596 543 592 633 198 63096 6054 11747 80897 Winter 2716 2553 2205 1226 980 842 709 546 672 651 689 884 620 660 672 472 8143 5995 2959 17097 Total 39656 60194 46674 26360 14100 7389 4089 2451 3897 2921 3116 3147 2679 2497 2509 1023 161587 28767 32348 222702 In different regions, the EC region reports the highest number of cases (approximately 51.88%), followed by the SW region (approximately 24.06%). Among the different types of malaria, relapsing malaria and unclassified malaria are primarily occurred in the EC region, while malignant malaria is mainly occurred in the SW region. In various provinces, the top five are mainly occurred in the southern regions of China, namely Anhui (45.33%), Yunnan (19.69%), Henan (8.54%), Hainan (6.40%), and Hubei (3.82%). In the CC region, cases are mainly occurred in Henan, in the SW region, they are mainly occurred in Yunnan, in the NW region, they are mainly occurred in Shaanxi, in the NE region, they are mainly occurred in Liaoning, in the EC region, they are mainly occurred in Anhui, in the NC region, they are mainly occurred in Hebei, and in the SC region, they are mainly occurred in Hainan ( Table 1 ) . 3.2 Emissions of GHGs and POPs As shown in Table S1 , the emission sources of various POPs are presented. In 2018, the total emissions of POPs in China mainly originated from Manufacturing Industries and Construction and Emissions from biomass burning. The sources of emissions for different POPs are generally similar to the overall emissions, with the exception of HCB, PCB, and PCDD_F, which primarily originate from Manufacturing Industries and Construction and Main Activity Electricity and Heat Production. As detailed in Table S2 , the emission sources of various GHGs are presented. In 2018, the top five sources of GHG emissions in China were Main Activity Electricity and Heat Production, Manufacturing Industries and Construction, Residential and Other Sectors, Road Transportation (no resuspension), and Cement Production. These five sectors jointly accounted for approximately 85.10% of the total GHG emissions. CH 4 primarily originates from Solid Fuels, Rice Cultivations, Enteric Fermentation, Solid Waste Disposal, and Wastewater Treatment and Discharge. N 2 O mainly comes from Direct N 2 O Emissions from Managed Soils, Main Activity Electricity and Heat Production, Indirect N 2 O Emissions from Managed Soils, Indirect N 2 O Emissions from the Atmospheric Deposition of Nitrogen in NOx and NH 3 , and Wastewater Treatment and Discharge. 3.3 Correlation among pollutants and malaria The results of the Spearman correlation analysis in Figure S1 indicate that the overall reported malaria cases show significant associations with all POPs, as well as with CO 2 among the GHGs. In different malaria types, both relapsing malaria and malignant malaria exhibit significant associations with all POPs. Relapsing malaria is significantly correlated with N 2 O and CO 2 from GHGs, while malignant malaria is significantly correlated with CH 4 and CO 2 bio from GHGs. Unclassified malaria, however, is only significantly associated with CH 4 and CO 2 from GHGs and does not show significant correlations with any of the POPs. 3.4 Prediction models 3.4.1 Prediction models: univariate time analysis Table 2 reflects the comparison of the predictive model performance for the overall and different types of malaria incidence during 2019–2020. In comparison with traditional time series models, the SARIMA model consistently outperforms the Holt-Winters model. The optimized model for the overall malaria incidence is SARIMA (1,1,0) (1,0,1) [12]. In contrast, the novel Kalman filter model demonstrates superior prediction performance compared to the traditional SARIMA model, showing improvements in both the overall and individual malaria types (MAE∈ [0.001, 0.016]). Table 2 Comparison of the three models in fitting performances and parameter estimations among different malaria. Model Series Best parameters RMSE MAE MAPE Holt-Winters Additive Model Total malaria α = 0.26, β = 0.03, γ = 0.98 0.088 0.051 99.874 SARIMA Multiple Model (1,1,0) (1,0,1) [12] 0.059 0.026 30.258 Kalman Model P0 = 1e6 0.036 0.016 18.740 Holt-Winters Additive Model Relapsing malaria α = 0.26, β = 0.03, γ = 0.97 0.075 0.043 173.480 SARIMA Multiple Model ( 3 , 1 , 2 ) (0,1,1) [12] 0.043 0.020 92.841 Kalman Model P0 = 1e6 0.031 0.013 25.122 Holt-Winters Additive Model Malignant malaria α = 0.38, β = 0.01, γ = 0.62 0.005 0.003 39.215 SARIMA Multiple Model ( 1 , 1 , 4 ) (1,0,1) [12] 0.004 0.002 23.004 Kalman Model P0 = 1e6 0.002 0.001 Inf Holt-Winters Additive Model Unclassified malaria α = 0.35, β = 0.03, γ = 1.00 0.013 0.008 236.830 SARIMA Multiple Model ( 2 , 1 , 1 ) (2,0,0) [12] 0.008 0.004 54.228 Kalman Model P0 = 1e6 0.005 0.002 19.800 From Fig. 1 , monthly prediction charts indicate that during periods of unusually high peak fluctuations, the Holt-Winters predicted values are closer to the actual values compared to the SARIMA model (Holt-Winters outperforms SARIMA in certain periods). However, during stable periods, the SARIMA model exhibits markedly reduced prediction fluctuations, thereby demonstrating superior forecasting performance. In the Kalman model prediction charts, the actual values and predicted values show significantly smaller fluctuations than in the other models (with the best prediction performance), and the effect is more evident during stable periods. As shown in Table S3 , for overall predictions, the standard error (|SE|=0.1597) of the SARIMA model is larger than that of the Holt-Winters model (|SE|=0.0626), while the Kalman model has the smallest standard error (|SE|=0.0061). As shown in Table S4 , for Relapsing malaria predictions, the standard error (|SE|=0.2721) of the SARIMA model is greater than that of the Holt-Winters model (|SE|=0.0971), with the Kalman model having the smallest standard error (|SE|=0.0022). As shown in Table S5 , for Malignant malaria predictions, the standard error of the SARIMA model is similar to that of the Holt-Winters model, but the Kalman model has the smallest standard error (|SE|=0.0043). As shown in Table S6 , for Unclassified malaria predictions, the standard error (|SE|=0.0404) of the Holt-Winters model is greater than that of the SARIMA model (|SE|=0.0345), while the Kalman model has the smallest standard error (|SE|=0.0004). For the 2019–2020 predicted values, only the Unclassified malaria model shows negative values based on the predictions from both the Holt-Winters and SARIMA models. 3.4.2 Prediction models: multivariate pollutants analysis Figure 2 reflects the evaluation of the predictive performance of POPs and GHGs for overall and different types of malaria. The evaluation is represented by the MAE (Mean Absolute Error) metric, where a smaller MAE indicates better model prediction performance. The models that perform best overall are primarily XGBOOST and SVM (Fig. 2 A). The MAE and R² of XGBoost on the validation set were 0.0599 and 0.616, respectively. For the Relapsing malaria type, the models that perform best are mainly SVM and XGBOOST (Fig. 2 B). For SVM, the MAE and R² on the validation set were 0.0432 and 0.679, respectively. For the Malignant malaria type, the models that perform best are primarily SVM and LSVM (Fig. 2 C). For SVM, the MAE and R² on the validation set were 0.0040 and 0.059, respectively. For the Unclassified malaria type, the models that perform best are mainly SVM and XGBOOST (Fig. 2 D). For SVM in a different comparison, the MAE and R² on the validation set were 0.0085 and 0.767, respectively. 3.5 Single POPs exposures and malaria For overall malaria, both PCB and HCB factors show significant risk levels for reported cases, regardless of whether greenhouse gases were adjusted for as confounders. Specifically, in the adjusted model, the risk levels for PCB and HCB are coefficients (95%CI): -1.48 (-2.69, -0.27) and − 1.39 (-2.57, -0.22), respectively. In the Relapsing malaria type, when confounders were not adjusted, only PCB and HCB showed significant risk levels. After adjusting for confounders, seven organic compounds, including BaP, BbF, BkF, IcdP, PCB, PCDD, and HCB, showed significant risk levels, with the risk levels for PCB and HCB being coefficients (95%CI): -1.13 (-1.77, -0.48) and − 1.05 (-1.65, -0.44), respectively. In the Malignant malaria type, when confounders were not adjusted, none of the organic compounds showed significant levels. However, after adjusting for confounders, five organic compounds, including BaP, BbF, BkF, IcdP, and PCDD, showed significant risk levels, with the risk levels for all these compounds ranging from − 0.10. In the Unclassified malaria type, when confounders were not adjusted, only PCB and HCB showed significant risk levels. After adjusting for confounders, seven organic compounds, including BaP, BbF, BkF, IcdP, PCB, PCDD, and HCB, showed significant risk levels, with the risk levels for PCB and HCB being coefficients (95%CI): -0.24 (-0.44, -0.05) and − 0.26 (-0.45, -0.06), respectively. The remaining five organic compounds (BaP, BbF, BkF, IcdP, PCDD) all showed positive risk levels in the adjusted model, with the risk levels ranging from coefficients: 0.10 to 0.14 (Table 3 ). Table 3 Multivariate WQS regression analysis of risk factors of malaria diseases among different groups. Stratification Predictor Variables Model 1 Model 2 Exp(β)(95%CI) P Exp(β)(95%CI) P Total malaria PCB -1.71 (-2.93, -0.49) 0.0072 ** -1.48 (-2.69, -0.27) 0.0186 * HCB -1.69 (-2.91, -0.47) 0.0077 ** -1.39 (-2.57, -0.22) 0.0217 * Relapsing malaria BaP -0.58 (-1.82, 0.67) 0.3680 -0.54 (-0.93, -0.14) 0.0088 ** BbF -0.80 (-2.12, 0.52) 0.2380 -0.56 (-0.95, -0.16) 0.0067 ** BkF -0.80 (-2.12, 0.51) 0.2350 -0.56 (-0.95, -0.16) 0.0066 ** IcdP -0.78 (-2.12, 0.56) 0.2580 -0.56 (-0.96, -0.16) 0.0073 ** PCB -1.43 (-2.44, -0.43) 0.0064 ** -1.13 (-1.77, -0.48) 0.0010 *** PCDD -0.80 (-2.13, 0.53) 0.2420 -0.56 (-0.96, -0.16) 0.0071 ** HCB -1.44 (-2.45, -0.43) 0.0063 ** -1.05 (-1.65, -0.44) 0.0010 *** Malignant malaria BaP -0.07 (-0.17, 0.03) 0.1486 -0.10 (-0.17, -0.02) 0.0108 * BbF -0.09 (-0.20, 0.02) 0.1106 -0.10 (-0.17, -0.03) 0.0090 ** BkF -0.09 (-0.20, 0.02) 0.1051 -0.10 (-0.17, -0.03) 0.0090 ** IcdP -0.09 (-0.19, 0.02) 0.1106 -0.10 (-0.18, -0.03) 0.0087 ** PCDD -0.09 (-0.19, 0.02) 0.1165 -0.10 (-0.18, -0.03) 0.0095 ** Unclassified malaria BaP -0.10 (-0.34, 0.14) 0.4080 0.10 (0.03, 0.16) 0.0073 ** BbF -0.14 (-0.39, 0.11) 0.2640 0.14 (0.06, 0.21) 0.0008 *** BkF -0.14 (-0.39, 0.11) 0.2620 0.14 (0.06, 0.22) 0.0008 *** IcdP -0.14 (-0.39, 0.11) 0.2790 0.13 (0.05, 0.21) 0.0011 ** PCB -0.27 (-0.47, -0.08) 0.0070 ** -0.24 (-0.44, -0.05) 0.0159 * PCDD -0.15 (-0.40, 0.11) 0.2620 0.13 (0.05, 0.20) 0.0012 ** HCB -0.28 (-0.48, -0.09) 0.0054 ** -0.26 (-0.45, -0.06) 0.0101 * Model 1: only single direction (positive or negative) POPs indicators. Model 2: combined all GHGs indicators for univariate analyzes. Bold font indicates statistical significance at the 0.05 level. Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 3.6 DLNM models 3.6.1 DLNM models: overall lagging effect As shown in Figure S2 , reported malaria cases in China demonstrate a nonlinear lagged association with the organic compounds PCB and HCB, where different lag months correspond to distinct epidemiological effects. Specifically, the lag effect of high-level PCB occurs rapidly but lasts for a short duration ( Figure S2 A, B ), whereas the lag effect of low-level HCB occurs slowly (risk emerging after approximately a 1-month lag) but persists for a longer duration ( Figure S2 C, D ). 3.6.2 DLNM models: exposure-reaction relationship Figure 3 shows the overall impact of two major persistent organic compounds (PCB and HCB) on the total number of cases for different types of malaria over a 6-month period. Overall, the relative risk of malaria increases with higher PCB levels (above 520T) and decreases with higher HCB levels. For relapsing malaria and unclassified malaria types, the relative risk of malaria increases with higher PCB levels (above 480T) and decreases with higher HCB levels. For malignant malaria, the relative risk of malaria increases with higher HCB levels (above 400T) and decreases with higher PCB levels. 3.6.3 DLNM models: extreme lagging cumulative effect Table 4 reflects the cumulative effects of the organic compounds PCB and HCB over different months (0–6 months). In overall malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 7.977 to 36,726.46 over the 0–6 months period. In contrast, extremely low levels of HCB had cumulative effect values of 3.602 (1.103, 11.765) in the first 3 months and 4.749 (1.11, 20.31) in the first 4 months, both of which increase the risk of malaria in the population. Extremely high levels of HCB showed a protective effect during the first 2 months. For relapsing malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 14.058 to 638,534.2 over the 0–6 months period. Extremely low levels of HCB had cumulative effect values of 3.164 (1.157, 8.651) in the first 2 months and 9.462 (1.087, 82.341) in the first 6 months, both increasing the risk of malaria in the population. Extremely high levels of HCB had a protective effect in the first month. For malignant malaria, there were no significant cumulative effect values, but both extremely low levels of PCB and extremely high levels of HCB may increase the risk of malaria. In unclassified malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 2.83 to 124,535.9 over the 0–6 months period. Extremely low levels of HCB had cumulative effect values of 2.944 (1.044, 8.3) in the first 3 months and 5.584 (1.113, 28.021) in the first 6 months, both increasing the risk of malaria in the population. Table 4 Cumulative effects of organic compounds PCB and HCB in each month (0–6 months). Series Cumulative risk (95% CI) Variables low PCB effect High PCB effect low HCB effect High HCB effect Total malaria Lag0 1.034 (0.789, 1.355) 7.977 (2.172, 29.29) 1.03 (0.722, 1.469) 0.27 (0.09, 0.813) Lag0-1 0.849 (0.481, 1.501) 76.349 (6.97, 836.301) 1.558 (0.851, 2.852) 0.057 (0.009, 0.383) Lag0-2 0.526 (0.221, 1.251) 949.048 (33.266, 27075.09) 2.4 (0.986, 5.838) 0.01 (0.001, 0.156) Lag0-3 0.335 (0.103, 1.093) 4687.511 (59.827, 367269.4) 3.602 (1.103, 11.765) 0.003 (0, 0.095) Lag0-4 0.254 (0.057, 1.135) 11706.39 (56.906, 2408162) 4.749 (1.11, 20.31) 0.001 (0, 0.083) Lag0-5 0.249 (0.042, 1.489) 21793.87 (46.533, 10207277) 5.048 (0.949, 26.859) 0.001 (0, 0.087) Lag0-6 0.328 (0.042, 2.546) 36726.46 (41.93, 32168849) 4.148 (0.667, 25.79) 0.001 (0, 0.1) Relapsing malaria Lag0 0.947 (0.73, 1.227) 14.058 (3.687, 53.595) 1.079 (0.737, 1.58) 0.151 (0.042, 0.549) Lag0-1 0.613 (0.342, 1.1) 138.465 (9.431, 2032.918) 1.772 (0.903, 3.478) 0.051 (0.004, 0.682) Lag0-2 0.294 (0.116, 0.746) 2056.348 (47.445, 89125.3) 3.164 (1.157, 8.651) 0.01 (0, 0.63) Lag0-3 0.152 (0.041, 0.558) 20062.42 (149.797, 2686977) 5.293 (1.357, 20.646) 0.004 (0, 1.156) Lag0-4 0.095 (0.018, 0.502) 111089.8 (273.58, 45109043) 7.725 (1.417, 42.1) 0.003 (0, 3.075) Lag0-5 0.075 (0.01, 0.554) 351631.7 (342.987, 360494091) 9.44 (1.318, 67.634) 0.003 (0, 7.486) Lag0-6 0.078 (0.008, 0.759) 638534.2 (340.063, 1198971679) 9.462 (1.087, 82.341) 0.003 (0, 12.814) Malignant malaria Lag0 1.687 (0.595, 4.782) 0.13 (0.001, 15.822) 0.954 (0.356, 2.556) 6.317 (0.026, 1514.783) Lag0-1 8.776 (0.271, 284.26) 0 (0, 768.387) 0.702 (0.029, 17.126) 20128.19 (0, 1.14E + 12) Lag0-2 124.882 (0.116, 134936.3) 0 (0, 15885.48) 0.243 (0, 212.675) 11133181828 (0, 5.49E + 25) Lag0-3 4202.652 (0.091, 193591131) 0 (0, 1218.606) 0.066 (0, 4357.513) 9.48E + 17 (0, 3.84E + 42) Lag0-4 115946.8 (0.087, 1.5422E + 11) 0 (0, 11.954) 0.02 (0, 76686.97) 4.54E + 25 (0, 1.87E + 58) Lag0-5 1078812 (0.084, 1.39E + 13) 0 (0, 0.321) 0.009 (0, 594626.3) 9.99E + 30 (0, 1.29E + 69) Lag0-6 2171216 (0.079, 5.97E + 13) 0 (0, 0.091) 0.006 (0, 1418617) 9.15E + 32 (0, 1.57E + 73) Unclassified malaria Lag0 0.809 (0.653, 1.003) 2.83 (1.152, 6.951) 1.143 (0.867, 1.508) 0.457 (0.198, 1.055) Lag0-1 0.462 (0.288, 0.74) 15.49 (2.497, 96.076) 1.365 (0.82, 2.271) 0.447 (0.094, 2.119) Lag0-2 0.174 (0.081, 0.374) 323.513 (23.124, 4525.97) 1.88 (0.87, 4.064) 0.249 (0.024, 2.596) Lag0-3 0.078 (0.026, 0.232) 3907.952 (121.723, 125465.6) 2.944 (1.044, 8.3) 0.104 (0.005, 2.199) Lag0-4 0.045 (0.011, 0.182) 22471.07 (329.054, 1534548) 4.442 (1.24, 15.916) 0.046 (0.001, 1.772) Lag0-5 0.031 (0.006, 0.168) 69762.35 (552.311, 8811678) 5.627 (1.292, 24.5) 0.028 (0, 1.721) Lag0-6 0.026 (0.004, 0.176) 124535.9 (629.074, 24654021) 5.584 (1.113, 28.021) 0.027 (0, 2.234) Bold font indicates statistical significance at the 0.05 level. Table S7 reflects the cumulative effects of the organic compounds PCB and HCB across all lag months (6 months). For both overall and relapsing malaria cases, the cumulative lag effects of extremely high PCB levels (P95) over a 0–6 month period were 3.673e + 4 (41.930, 3.217e + 7) and 6.385e + 5 (340.063, 1.199e + 9), respectively, indicating a significant elevation in malaria risk. Additionally, for low levels of HCB (P75-P95), the cumulative lag effects over 0–6 months were 9.462 (1.087, 82.341) and 2.889 (1.005, 8.310), respectively, both of which increase the risk of developing malaria in relapsing malaria patients. For malignant malaria, high levels of HCB (P75) corresponded to a cumulative lag effect over 0–6 months of 3.307e + 46 (11.764, 9.296e + 91), which increases the risk of developing malaria in patients. In unclassified malaria, both high levels of PCB (P75-P95) and low levels of HCB (P5-P25) corresponded to cumulative lag effects greater than 1, which increases the risk of developing malaria in patients. 3.6.4 DLNM models: extreme lagging relative effect Figure 4 reflects the current-month lag effects of the organic compounds PCB and HCB across different months. In both overall malaria and Relapsing malaria types, the lag effect of low-level PCB in the 6th month showed a significant positive risk level. High-level PCB and low-level HCB exhibited significant risk levels in the 1st to 4th months, with the lag effect in the 2nd month showing the highest peak. In malignant malaria, there were no significant single-month lag effects. In unclassified malaria, high-level PCB and low-level HCB showed significant risk levels in the 2nd to 5th months, with the highest peak lag effect observed in the 2nd and 3rd months, respectively. 3.6.5 DLNM models: sensitivity analysis Based on the QAIC (Quasi-Akaike Information Criterion) selection criterion, the optimal maximum lag period was determined to be 6 months. According to the minimum partial autocorrelation function (PACF) criterion, the derived parameters exhibited variation across different malaria types. In the overall patient population, the degrees of freedom (df) for the time variable were 2, for PCB was 6, and for HCB was 4. In relapsing malaria patients, the degrees of freedom for the time variable were 2, for PCB was 6, and for HCB was 5. In malignant malaria patients, the degrees of freedom for the time variable were 5, for PCB was 6, and for HCB was 7. In unclassified malaria patients, the degrees of freedom for the time variable were 2, for PCB was 4, and for HCB was 4. 4. Discussion Malaria in China predominantly occurs during the summer and autumn seasons, driven by warm, humid conditions that favor mosquito breeding and the transmission of Plasmodium pathogens( 25 ). This seasonal pattern aligns with findings from a Yunnan-based study( 26 ). Geographically, malaria in China is concentrated in the southwestern and eastern regions, suggesting that climatic factors—particularly in the southwest (e.g., Yunnan Province), where tropical and subtropical conditions prevail—play a critical role. High temperatures and humidity accelerate mosquito reproduction, while the rainy season creates stagnant water bodies (e.g., ponds) that serve as ideal breeding sites, thereby increasing malaria transmission risk. Regarding malaria types, Plasmodium vivax (tertian malaria) is the predominant form in China, owing to the widespread distribution of Anopheles mosquitoes, particularly Anopheles sinensis, a highly competent vector for P. vivax( 27 ). Currently, the primary source of malignant malaria is imported malaria. Studies have found that during China’s industrialization process, industries such as coal combustion, oil refining, and steel production have emerged. In these sectors, coal, as one of the main energy sources, releases large amounts of polycyclic aromatic hydrocarbons during combustion( 28 ), especially in regions using incomplete combustion equipment and technologies, where emissions are more severe. This aligns with the findings of the study, which identifies manufacturing and construction industries as the primary sources of China's POPs, followed by biomass burning emissions. The study also found that PCB emissions primarily stem from electricity and thermal power generation, consistent with the findings of a study conducted along the Indus River( 29 ). In this study, SARIMA seasonal model is more effective in detecting subtle differences during stable periods, thereby achieving stable predictions. This finding is consistent with previous research results( 18 ). The study also applied the Kalman filter model showed superior performance. This is in agreement with the results of Sangil Kim and others in predicting Hand, Foot, and Mouth Disease( 30 ). In the multivariate predictive analysis, the SVM and XGBoost models were found to be more suitable for predicting the impact of POPs on malaria incidence in China, which is supported by similar findings in several studies( 24 , 31 ). This study revealed that the risk of malaria incidence associated with POPs is substantially amplified under the mediating influence of GHGs. Additionally, an experimental study demonstrating elevated microbial respiration (manifested as increased CO₂ production) observed a corresponding rise in the release of organic pollutants into aquatic systems( 32 ). This might be due to certain greenhouse gases (such as CO 2 ) facilitating the release of POPs, thereby enhancing the risk of malaria. In detailed studies, we observed that PAH congeners and PCDD exhibited positive risk effects on the incidence of different malaria types. The most common pollutants in urban environments include heavy metals, PAHs, and others. These pollutants persist in the environment and can accumulate in air, water, and soil, leading to complex interactions affecting urban ecosystems( 33 ). This finding is consistent with a study conducted in Africa, which revealed that polycyclic aromatic hydrocarbons (PAHs) can drive insecticide resistance in malaria vectors, thereby indirectly elevating the potential risk of malaria transmission and incidence( 34 ). Therefore, aquatic and terrestrial organisms in these ecosystems constantly absorb these substances, which may result in bioaccumulation and impact their behavior and reproduction( 35 , 36 ). Due to the key role of mosquitoes in decomposition and the food web, as well as their utility as environmental pollution sentinels, they accumulate organic pollutants in their tissues. These pollutants, such as PAHs, usually affect their adult stages and may influence mosquito reproduction( 37 , 38 ). A U.S. study found that direct exposure to dioxin-like organic compounds could progressively increase the susceptibility of separated human red blood cells to Plasmodium falciparum after 48 hours( 39 ). This finding is similar to our observation that organic pollutants like PCDD can increase the risk of malaria. The lag analysis showed that long-term PCB exposure was positively associated with increased malaria risk, as previously discussed. Several prior studies have confirmed that compounds like DDT can control malaria transmission by killing pathogen larvae( 40 ). This is similar to the finding in this study that HCB shows a protective effect on malaria incidence under long-term lag (with a dose-response relationship showing negative feedback). While prior studies have primarily examined how organic pollutants (e.g., DDT) may mitigate malaria transmission, the role of POPs in enhancing transmission has been overlooked. This study addresses this gap by analyzing GHGs and their mediating influence on pollutant impacts. For predictive modeling, we improve upon traditional approaches by integrating the Kalman filter, enabling high-precision forecasts without reliance on fluctuation amplitude. Furthermore, we broke through traditional meteorological and pollution model analysis to explore the lag effects of environmental factors, analyzing the long-term lag effects of organic pollutants on malaria risk. However, due to data limitations, further research is needed on other malaria subtypes. There is some collinearity among various POPs factors; however, these factors were included in the analysis due to their statistical significance, in order to draw reasonable conclusions. This issue of collinearity may be addressed in future environmental health studies using the Qgcomp analysis method. POP emission data do not directly represent human exposure. Ecological exposure estimates may introduce measurement error and ecological bias. Regarding etiological risks, there is a lack of data on pathogens such as Plasmodium, which highlights the need for future scholars to collect pathogen-level data and establish animal and statistical models to investigate the etiological and transmission mechanisms, in order to determine causal relationships. 5. Conclusion We analyzed malaria surveillance and early warning in mainland China, along with lagged effects of organic pollutants on malaria incidence. Extreme levels of organic pollutants (PCB, HCB) are associated with increased malaria transmission risk, with varying transmission peaks. Strengthening POPs emission control activities during these periods may help reduce the risk of seasonal malaria susceptibility. Declarations Ethics approval and consent to participate Malaria is classified as a Class B infectious disease under China's Infectious Disease Prevention and Control Law. This study was approved by the Ethics Committee of the Chinese Center for Disease Control and Prevention (CDC). For confidentiality purposes, all data were analyzed anonymously. Due to the mandatory monitoring required by China's Infectious Disease Prevention and Control Law and the public database, patient consent is not required. All data usage strictly complies with the "Data Security Law of the People's Republic of China" and the "Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases." Official permission has been obtained from the China CDC. The authorization document clearly specifies that the data usage is limited to aggregated incidence rate statistics and does not involve individual patient privacy information, fully complying with public health data security management regulations. Consent for publication All authors have consented to publication of this research. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgements The disease data was publicly supported from the National Public Health Data Centre of China (https://www.phsciencedata.cn/). Pollutants information was publicly from the Emissions Database for Global Atmospheric Research (EDGAR) (https://edgar.jrc.ec.europa.eu/). Availability of data and material The data that support the findings of this study are available on request from the National Public Health Data Centre of China (https://www.phsciencedata.cn/) and the the Emissions Database for Global Atmospheric Research (EDGAR) (https://edgar.jrc.ec.europa.eu/). CRediT authorship contribution statement Guolong Qu: Software, Conceptual, Methodology, Formal analysis, Investigation, Resources, Writing-original draft, Writing-review & editing. Jianqiang Han: Software, Conceptual, Methodology, Formal analysis, Investigation, Writing-original draft, Writing-review & editing. Zhenyao Song: Conceptualization, Methodology, Formal analysis, Writing-review & editing, Supervision. Weiming Hou: Conceptualization, Methodology, Formal analysis, Writing-review & editing, Funding acquisition, Supervision. 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16:44:23","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":357707,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/e128ee779eb5354115925750.html"},{"id":100359553,"identity":"ee919961-4e35-4d16-980a-b09e261b73c6","added_by":"auto","created_at":"2026-01-16 07:23:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":350160,"visible":true,"origin":"","legend":"\u003cp\u003eFitting status between the actual incidence of different malaria in China mainland from Jan 2005 to Dec 2018 and the predicted incidence from Jan 2019 to Dec 2020 using the preferred three models. The deep shaded regions indicate 80% confidence intervals, the light shaded regions indicate 95% confidence intervals.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/be2017be829d5665ba3f0985.png"},{"id":99905601,"identity":"77d440ce-413f-4a21-818b-9ae7634245dd","added_by":"auto","created_at":"2026-01-09 16:44:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":147834,"visible":true,"origin":"","legend":"\u003cp\u003ePrediction performance metrics comparison in different malaria for different machine learning algorithms.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/156058111444ff8245494d89.png"},{"id":99905602,"identity":"9625e56a-0250-4518-b988-c8becf745bc6","added_by":"auto","created_at":"2026-01-09 16:44:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":159116,"visible":true,"origin":"","legend":"\u003cp\u003eOverall relationship of in different malaria risks with POPs indicators in China mainland, with the median of each exposure serving as the reference value.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/249dc31252cb1944a9338da7.png"},{"id":99905606,"identity":"129bf5fd-99f1-4e46-83b9-3c6c5c1b33d2","added_by":"auto","created_at":"2026-01-09 16:44:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":222868,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of estimated extreme effects at the 5th and the 95th percentile of PCB and HCB on different malaria cases at different lag months.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/cc7433be6211fa82a3680e3b.png"},{"id":101942680,"identity":"145e02c5-281d-48ce-9eed-d3d017e344ac","added_by":"auto","created_at":"2026-02-05 09:33:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2972159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/4c5645f8-e204-42b5-a7fb-ffb081209724.pdf"},{"id":99905600,"identity":"3bb685ff-5e82-4d86-b2fc-b85e673bc404","added_by":"auto","created_at":"2026-01-09 16:44:22","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":386268,"visible":true,"origin":"","legend":"","description":"","filename":"Supply.docx","url":"https://assets-eu.researchsquare.com/files/rs-8542332/v1/aa6763b4aaad2cdd0c0124f7.docx"}],"financialInterests":"","formattedTitle":"Effect of Persistent Organic Pollutants on malaria in Chinese mainland: a population-based surveillance and modelling study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMalaria is a mosquito-borne infectious parasitic disease prevalent in tropical and subtropical regions(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In 2021, nearly half of the global population faced the risk of malaria, with an estimated 247\u0026nbsp;million cases and 619,000 deaths worldwide(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Malaria incidence shows a seasonal trend, and time series models are the most commonly used cyclical models for disease prediction. Several studies have shown that the Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model performs well in predicting malaria incidence across different countries(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). However, existing research predominantly focuses on model self-comparisons, with limited systematic evaluation of different time series methodologies or the development of novel modeling approaches\u0026mdash;representing a significant gap in the field.\u003c/p\u003e \u003cp\u003eEnvironmental factors play a crucial role in the dynamics and distribution of malaria(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). From a biological perspective, meteorological factors are intrinsically linked to malaria incidence through their influence on mosquito vectors and the development of Plasmodium within the vectors. Studies have shown that under colder environmental conditions, larger daily temperature fluctuations accelerate malaria incidence(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Additionally, research has confirmed that the increase in malaria incidence occurs after a 30-day interval following the simultaneous occurrence of high temperatures and rainfall(\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Although a Singaporean survey revealed an inverse association between ground-level SO₂ and PM₂.₅ concentrations and mosquito-borne disease incidence - investigating chemical pollutants as risk factors for malaria (a naturally occurring zoonotic disease) - remains epidemiologically valuable(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). This is reflected in the complex network of relationships between various environmental factors and diseases. Moreover, when modeling the impact of environmental factors on malaria cases, two key issues need special attention: non-linear risk effects and lag effects, which are seldom considered together in some studies.\u003c/p\u003e \u003cp\u003ePersistent Organic Pollutants (POPs) have a long history of application in commercial and industrial sectors. Individuals can be exposed to them through daily activities, dietary intake, and drinking water(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). The concentrations of PCBs in the Lanzhou section of the Yellow River, China, fluctuate within the range of 3.08 to 32.3 ng/g, dominated by fully chlorinated congeners, with industrial activities identified as the primary source(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Multi-regional studies conducted in southeastern and northeastern China have revealed that 66.7% of organochlorine pesticides (OCPs) exhibit a detection frequency exceeding 80%, indicating their ubiquity in the atmosphere. Notably, hexachlorobenzene (HCB) was found to have the highest concentration across all sampling sites(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). However, most studies have focused on direct exposure pathways, such as the relationship between POPs exposure in internal environments (e.g., blood) and diseases, which has shown some correlation between POPs and disease(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). However, these studies have overlooked the long-term exposure risks from external environments, thus neglecting the potential harms of indirect exposure. Although some studies have explored the carcinogenic and non-carcinogenic risks of POPs in the air, they have not concluded that they pose a health threat to humans(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Additionally, previous animal studies evaluated mice fed with polychlorinated biphenyls (PCBs) for 3 to 6 weeks and subsequently inoculated with Plasmodium berghei (murine malaria parasite). The results demonstrated a 20% reduction in the average survival time of the exposed mice, which preliminarily indicated that environmental chemical pollutants could impair host resistance. It was hypothesized that the assessment of immune parameters might serve as a sensitive indicator for the toxic effects of such pollutants(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Current evidence fails to establish a clear association between malaria (a parasitic disease) and POPs, making this research particularly timely.\u003c/p\u003e \u003cp\u003eAccordingly, this study employs a systematic approach comprising: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) a three-dimensional spatial-temporal distribution analysis of malaria incidence cases across China; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) a comprehensive descriptive analysis of domestic POPs emission sources; and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a two-stage predictive framework integrating incidence risk forecasting for malaria strains with subsequent multi-stage effect analysis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.1 Data Source and mapping of malaria cases in different regions\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eData on all reported malaria cases in mainland China from 2005 to 2020 were obtained from the Public Health Science Data Center Website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.phsciencedata.cn/\u003c/span\u003e\u003cspan address=\"https://www.phsciencedata.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). POPs (such as Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Benzo[k]fluoranthene (BkF), Indeno[1,2,3-cd]pyrene (IcdP), Polychlorinated Biphenyls (PCB), Polychlorinated Dibenzo-p-dioxins (PCDD), and Hexachlorobenzene (HCB) ) as well as Greenhouse Gases (GHGs) (including Methane (CH\u003csub\u003e4\u003c/sub\u003e), Biogenic Carbon Dioxide (CO\u003csub\u003e2\u003c/sub\u003ebio), Nitrous Oxide (N\u003csub\u003e2\u003c/sub\u003eO) and Carbon Dioxide (CO\u003csub\u003e2\u003c/sub\u003e) ) were sourced from the Emissions Database for Global Atmospheric Research (EDGAR) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://edgar.jrc.ec.europa.eu/\u003c/span\u003e\u003cspan address=\"https://edgar.jrc.ec.europa.eu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). According to geographical conditions, China is divided into seven regions in the planning: North China (NC), Northeast China (NE), East China (EC), Central China (CC), South China (SC), Southwest China (SW), and Northwest China (NW).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Different fields and sources of POPs and GHGs\u003c/h2\u003e \u003cp\u003eThis study summarizes the emissions of POPs and GHGs using the EDGAR database, explores and ranks the main sources of these pollutants and industry sectors, and further analyzes the causes of pollutant generation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Features selection\u003c/h2\u003e \u003cp\u003eSpearman correlation analysis was performed to examine associations between various POPs, GHGs, and the incidence of different malaria types. Analysis prioritized relationships with organic pollutants. For malaria subtypes exhibiting no significant correlations with any POPs, subsequent analyses focused on significant factors identified in other malaria types, ultimately selecting consistently significant variables across multiple malaria types for in-depth investigation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Prediction analysis\u003c/h2\u003e \u003cp\u003eIn the univariate prediction, this study uses time series analysis models to explore the effect of time variables on the prediction of incidence rates for different types of malaria, thereby accurately monitoring the disease. The time series models selected include Holt-Winters, SARIMA, and Kalman filter models.\u003c/p\u003e \u003cp\u003eSARIMA is a model that combines seasonal differences with the ARIMA model, effectively modeling time series data with cyclical patterns. SARIMA (p, d, q) (P, D, Q) s includes seven parameters, which can be divided into two groups: three non-seasonal parameters (p, d, q) and four seasonal parameters (P, D, Q) s. The Holt-Winters exponential smoothing model eliminates some random fluctuations, assigning different weights to data from each period to predict future trends(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The Holt-Winters exponential smoothing model and the SARIMA model have been more specifically introduced in previous studies(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Kalman filtering is an optimal recursive estimation algorithm that estimates the current system state by integrating prior state estimates with current observations, fundamentally relying on state-space equations and Bayesian recursive estimation principles. Therefore, Kalman filtering does not require stationarity or time-varying signals. Specific formulas and algorithms can be found in previous research(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). For the univariate model prediction effectiveness, we compared the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Standard Error (SE) indicators. This model was implemented using the \"forecast\" and \"FKF\" packages in R software version 4.4.1.\u003c/p\u003e \u003cp\u003eIn the multivariate prediction, this study employs various machine learning algorithms to explore the predictive effects of POPs and GHGs on the incidence of different types of malaria. A 70% dataset was used as the training set for multi-model comparison, while the remaining 30% dataset was used as the validation set to test the optimal algorithm. The machine learning algorithms used include Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Linear Support Vector Machine (LSVM), Linear Model (LM), and Neural Network (NNET). Specific algorithms can be found in the relevant literature references(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Cross-validation was performed using the vfold_cv() function, followed by multi-model fitting and comparison after the cross-validation. For the multivariate model prediction effectiveness, we compared the MAE indicator. This model was implemented using the \"tidyverse,\" \"kernlab,\" \"nnet,\" \"bonsai,\" \"lightgbm,\" and \"xgboost\" packages in R software version 4.4.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Risk analysis\u003c/h2\u003e \u003cp\u003eThe Weighted Quantile Sum (WQS) regression is used to examine the individual contributions of POP mixtures. The WQS model is widely applied to analyze the effects of multiple chemical exposures on human health(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In the present study, the model was set to 5000 iterations, with significant organic compounds identified in the preliminary feature extraction step being used as the main effects. Two comparison models were also set up: Model 1 without confounding factors, and Model 2 with significant greenhouse gases as confounding factors. Based on an index that includes all persistent organic pollutants, WQS regression was performed in both positive and negative directions. The weight of each organic compound ranges from 0 to 1, with the sum of the weights equaling 1(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The WQS coefficient is an indicator of the association strength between the weighted quantile score and the outcome variable, essentially reflecting the \"overall effect\" of the mixture. The sign of the coefficient corresponds to the direction of the effect, while the absolute value reflects the strength of the effect. The study represents the risk coefficient by calculating the coefficients of individual pollutant factors. This model was implemented using the \"gWQS\" package in R software version 4.4.1.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Lagging effect\u003c/h2\u003e \u003cp\u003eWe used Distributed Lag Nonlinear Model (DLNM) to study the impact of monthly exposure to each organic compound on malaria incidence, while controlling for time variables (month) and other air pollutants, such as greenhouse gases, as confounding factors. We modeled each organic pollutant using 1 to 6 knots and selected the best-fitting model using the Quasi-Akaike Information Criterion (Q-AIC), which was also used to calculate the optimal lag months(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). In this study, the optimal lag period was 6 months(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Furthermore, we calculated the cumulative effect and relative effect by exploring the relationship between each organic compound at specific levels and specific lag months. For the cumulative effect of organic compounds across all lag months (6 months), specific percentiles were chosen at P5, P25, P75, and P95 levels. For the lag effects at each month and cumulative effects across the months (0\u0026ndash;6 months), the specific percentiles were selected at P5 and P95 levels. In addition, sensitivity analysis was performed on this model to determine its parameters. The degrees of freedom for the lag variables were set to df\u0026thinsp;=\u0026thinsp;3 based on previous literature and customary practices. This model was implemented using the \"dlnm\" package in R software version 4.4.1.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Epidemiological characteristics of malaria\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, the overall reported malaria cases exhibit a declining trend over the years, with two anomalous peaks of increased incidence occurring in 2006 and 2013. Additionally, there was a short-term increase in reported cases between 2014 and 2016. The seasonal characteristics reveal an epidemic pattern in the summer and autumn (approximately 77.35%), with the majority of cases occurring in the summer (about 41.03%). When comparing the reported cases of different types of malaria, relapsing malaria predominates (about 72.56%), followed by unclassified malaria (about 14.52%), and malignant malaria (about 12.92%). Relapsing malaria and unclassified malaria are mainly prevalent in the summer and autumn, while malignant malaria is more prevalent in the spring and summer.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe regional and temporal distribution of different types of malaria in mainland China from 2005 to 2020.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"21\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDistributions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"16\"\u003e\n \u003cp\u003eYear\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eSeries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapsing malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnclassified malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6970\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23521\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHenan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16830\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHubei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e429\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHunan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e213\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3870\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e777\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11260\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53587\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYunnan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6442\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e516\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e331\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43849\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuizhou\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e466\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e961\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e397\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2979\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSichuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e495\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e312\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e339\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4066\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChongqing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXizang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e399\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1368\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShaanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e453\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e857\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGansu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eXinjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNingxia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQinghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e175\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiaoning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e615\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJilin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e211\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHeilongjiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e996\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15949\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115538\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnhui\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15693\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e27307\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e644\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e85877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e100950\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJiangsu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e362\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e402\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e721\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhejiang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e404\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3530\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShandong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e247\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2481\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFujian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1116\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShanghai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJiangxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e593\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e431\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHebei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBeijing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShanxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTianjin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeimenggu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4617\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19899\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHainan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3853\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1844\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6918\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14250\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangxi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e387\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3701\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGuangdong\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e559\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1948\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSeasons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSpring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e619\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e714\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e522\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20367\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7802\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5169\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33338\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15533\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2955\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e758\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e91370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAutumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3739\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e528\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e600\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e543\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e63096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11747\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e980\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8143\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2959\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17097\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3116\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e161587\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e28767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32348\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222702\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eIn different regions, the EC region reports the highest number of cases (approximately 51.88%), followed by the SW region (approximately 24.06%). Among the different types of malaria, relapsing malaria and unclassified malaria are primarily occurred in the EC region, while malignant malaria is mainly occurred in the SW region. In various provinces, the top five are mainly occurred in the southern regions of China, namely Anhui (45.33%), Yunnan (19.69%), Henan (8.54%), Hainan (6.40%), and Hubei (3.82%). In the CC region, cases are mainly occurred in Henan, in the SW region, they are mainly occurred in Yunnan, in the NW region, they are mainly occurred in Shaanxi, in the NE region, they are mainly occurred in Liaoning, in the EC region, they are mainly occurred in Anhui, in the NC region, they are mainly occurred in Hebei, and in the SC region, they are mainly occurred in Hainan \u003cstrong\u003e(\u003c/strong\u003eTable \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cstrong\u003e)\u003c/strong\u003e.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Emissions of GHGs and POPs\u003c/h2\u003e\n \u003cp\u003eAs shown in \u003cstrong\u003eTable S1\u003c/strong\u003e, the emission sources of various POPs are presented. In 2018, the total emissions of POPs in China mainly originated from Manufacturing Industries and Construction and Emissions from biomass burning. The sources of emissions for different POPs are generally similar to the overall emissions, with the exception of HCB, PCB, and PCDD_F, which primarily originate from Manufacturing Industries and Construction and Main Activity Electricity and Heat Production. As detailed in \u003cstrong\u003eTable S2\u003c/strong\u003e, the emission sources of various GHGs are presented. In 2018, the top five sources of GHG emissions in China were Main Activity Electricity and Heat Production, Manufacturing Industries and Construction, Residential and Other Sectors, Road Transportation (no resuspension), and Cement Production. These five sectors jointly accounted for approximately 85.10% of the total GHG emissions. CH\u003csub\u003e4\u003c/sub\u003e primarily originates from Solid Fuels, Rice Cultivations, Enteric Fermentation, Solid Waste Disposal, and Wastewater Treatment and Discharge. N\u003csub\u003e2\u003c/sub\u003eO mainly comes from Direct N\u003csub\u003e2\u003c/sub\u003eO Emissions from Managed Soils, Main Activity Electricity and Heat Production, Indirect N\u003csub\u003e2\u003c/sub\u003eO Emissions from Managed Soils, Indirect N\u003csub\u003e2\u003c/sub\u003eO Emissions from the Atmospheric Deposition of Nitrogen in NOx and NH\u003csub\u003e3\u003c/sub\u003e, and Wastewater Treatment and Discharge.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 Correlation among pollutants and malaria\u003c/h2\u003e\n \u003cp\u003eThe results of the Spearman correlation analysis in \u003cstrong\u003eFigure S1\u003c/strong\u003e indicate that the overall reported malaria cases show significant associations with all POPs, as well as with CO\u003csub\u003e2\u003c/sub\u003e among the GHGs. In different malaria types, both relapsing malaria and malignant malaria exhibit significant associations with all POPs. Relapsing malaria is significantly correlated with N\u003csub\u003e2\u003c/sub\u003eO and CO\u003csub\u003e2\u003c/sub\u003e from GHGs, while malignant malaria is significantly correlated with CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003ebio from GHGs. Unclassified malaria, however, is only significantly associated with CH\u003csub\u003e4\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e from GHGs and does not show significant correlations with any of the POPs.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 Prediction models\u003c/h2\u003e\n \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.1 Prediction models: univariate time analysis\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reflects the comparison of the predictive model performance for the overall and different types of malaria incidence during 2019\u0026ndash;2020. In comparison with traditional time series models, the SARIMA model consistently outperforms the Holt-Winters model. The optimized model for the overall malaria incidence is SARIMA (1,1,0) (1,0,1) [12]. In contrast, the novel Kalman filter model demonstrates superior prediction performance compared to the traditional SARIMA model, showing improvements in both the overall and individual malaria types (MAE\u0026isin; [0.001, 0.016]).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eComparison of the three models in fitting performances and parameter estimations among different malaria.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBest parameters\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRMSE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMAPE\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHolt-Winters Additive Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eTotal malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;0.26, \u0026beta;\u0026thinsp;=\u0026thinsp;0.03, \u0026gamma;\u0026thinsp;=\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e99.874\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIMA Multiple Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(1,1,0) (1,0,1) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30.258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalman Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP0\u0026thinsp;=\u0026thinsp;1e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.740\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHolt-Winters Additive Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eRelapsing malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;0.26, \u0026beta;\u0026thinsp;=\u0026thinsp;0.03, \u0026gamma;\u0026thinsp;=\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173.480\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIMA Multiple Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e) (0,1,1) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e92.841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalman Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP0\u0026thinsp;=\u0026thinsp;1e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25.122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHolt-Winters Additive Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eMalignant malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;0.38, \u0026beta;\u0026thinsp;=\u0026thinsp;0.01, \u0026gamma;\u0026thinsp;=\u0026thinsp;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39.215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIMA Multiple Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e) (1,0,1) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalman Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP0\u0026thinsp;=\u0026thinsp;1e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInf\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHolt-Winters Additive Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" rowspan=\"3\"\u003e\n \u003cp\u003eUnclassified malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026alpha;\u0026thinsp;=\u0026thinsp;0.35, \u0026beta;\u0026thinsp;=\u0026thinsp;0.03, \u0026gamma;\u0026thinsp;=\u0026thinsp;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236.830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSARIMA Multiple Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e) (2,0,0) [12]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e54.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKalman Model\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eP0\u0026thinsp;=\u0026thinsp;1e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.800\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eFrom Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, monthly prediction charts indicate that during periods of unusually high peak fluctuations, the Holt-Winters predicted values are closer to the actual values compared to the SARIMA model (Holt-Winters outperforms SARIMA in certain periods). However, during stable periods, the SARIMA model exhibits markedly reduced prediction fluctuations, thereby demonstrating superior forecasting performance. In the Kalman model prediction charts, the actual values and predicted values show significantly smaller fluctuations than in the other models (with the best prediction performance), and the effect is more evident during stable periods. As shown in \u003cstrong\u003eTable S3\u003c/strong\u003e, for overall predictions, the standard error (|SE|=0.1597) of the SARIMA model is larger than that of the Holt-Winters model (|SE|=0.0626), while the Kalman model has the smallest standard error (|SE|=0.0061). As shown in \u003cstrong\u003eTable S4\u003c/strong\u003e, for Relapsing malaria predictions, the standard error (|SE|=0.2721) of the SARIMA model is greater than that of the Holt-Winters model (|SE|=0.0971), with the Kalman model having the smallest standard error (|SE|=0.0022). As shown in \u003cstrong\u003eTable S5\u003c/strong\u003e, for Malignant malaria predictions, the standard error of the SARIMA model is similar to that of the Holt-Winters model, but the Kalman model has the smallest standard error (|SE|=0.0043). As shown in \u003cstrong\u003eTable S6\u003c/strong\u003e, for Unclassified malaria predictions, the standard error (|SE|=0.0404) of the Holt-Winters model is greater than that of the SARIMA model (|SE|=0.0345), while the Kalman model has the smallest standard error (|SE|=0.0004). For the 2019\u0026ndash;2020 predicted values, only the Unclassified malaria model shows negative values based on the predictions from both the Holt-Winters and SARIMA models.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\n \u003ch2\u003e3.4.2 Prediction models: multivariate pollutants analysis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e reflects the evaluation of the predictive performance of POPs and GHGs for overall and different types of malaria. The evaluation is represented by the MAE (Mean Absolute Error) metric, where a smaller MAE indicates better model prediction performance. The models that perform best overall are primarily XGBOOST and SVM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA). The MAE and R\u0026sup2; of XGBoost on the validation set were 0.0599 and 0.616, respectively. For the Relapsing malaria type, the models that perform best are mainly SVM and XGBOOST (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB). For SVM, the MAE and R\u0026sup2; on the validation set were 0.0432 and 0.679, respectively. For the Malignant malaria type, the models that perform best are primarily SVM and LSVM (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC). For SVM, the MAE and R\u0026sup2; on the validation set were 0.0040 and 0.059, respectively. For the Unclassified malaria type, the models that perform best are mainly SVM and XGBOOST (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD). For SVM in a different comparison, the MAE and R\u0026sup2; on the validation set were 0.0085 and 0.767, respectively.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Single POPs exposures and malaria\u003c/h2\u003e\n \u003cp\u003eFor overall malaria, both PCB and HCB factors show significant risk levels for reported cases, regardless of whether greenhouse gases were adjusted for as confounders. Specifically, in the adjusted model, the risk levels for PCB and HCB are coefficients (95%CI): -1.48 (-2.69, -0.27) and \u0026minus;\u0026thinsp;1.39 (-2.57, -0.22), respectively. In the Relapsing malaria type, when confounders were not adjusted, only PCB and HCB showed significant risk levels. After adjusting for confounders, seven organic compounds, including BaP, BbF, BkF, IcdP, PCB, PCDD, and HCB, showed significant risk levels, with the risk levels for PCB and HCB being coefficients (95%CI): -1.13 (-1.77, -0.48) and \u0026minus;\u0026thinsp;1.05 (-1.65, -0.44), respectively. In the Malignant malaria type, when confounders were not adjusted, none of the organic compounds showed significant levels. However, after adjusting for confounders, five organic compounds, including BaP, BbF, BkF, IcdP, and PCDD, showed significant risk levels, with the risk levels for all these compounds ranging from \u0026minus;\u0026thinsp;0.10. In the Unclassified malaria type, when confounders were not adjusted, only PCB and HCB showed significant risk levels. After adjusting for confounders, seven organic compounds, including BaP, BbF, BkF, IcdP, PCB, PCDD, and HCB, showed significant risk levels, with the risk levels for PCB and HCB being coefficients (95%CI): -0.24 (-0.44, -0.05) and \u0026minus;\u0026thinsp;0.26 (-0.45, -0.06), respectively. The remaining five organic compounds (BaP, BbF, BkF, IcdP, PCDD) all showed positive risk levels in the adjusted model, with the risk levels ranging from coefficients: 0.10 to 0.14 (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eMultivariate WQS regression analysis of risk factors of malaria diseases among different groups.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eStratification\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003ePredictor Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExp(\u0026beta;)(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eExp(\u0026beta;)(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eTotal malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.71 (-2.93, -0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0072 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.48 (-2.69, -0.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0186 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.69 (-2.91, -0.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0077 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.39 (-2.57, -0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0217 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eRelapsing malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.58 (-1.82, 0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.3680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.54 (-0.93, -0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0088 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBbF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.80 (-2.12, 0.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56 (-0.95, -0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0067 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBkF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.80 (-2.12, 0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56 (-0.95, -0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0066 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIcdP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.78 (-2.12, 0.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56 (-0.96, -0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0073 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.43 (-2.44, -0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0064 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.13 (-1.77, -0.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0010 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.80 (-2.13, 0.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.56 (-0.96, -0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0071 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.44 (-2.45, -0.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0063 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-1.05 (-1.65, -0.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0010 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003eMalignant malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.07 (-0.17, 0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1486\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.17, -0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0108 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBbF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09 (-0.20, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.17, -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0090 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBkF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09 (-0.20, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.17, -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0090 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIcdP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09 (-0.19, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.18, -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0087 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.09 (-0.19, 0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1165\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.18, -0.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0095 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eUnclassified malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.10 (-0.34, 0.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.4080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.10 (0.03, 0.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0073 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBbF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.39, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 (0.06, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBkF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.39, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 (0.06, 0.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0008 ***\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIcdP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.39, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (0.05, 0.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0011 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.27 (-0.47, -0.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0070 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24 (-0.44, -0.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0159 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePCDD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15 (-0.40, 0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.2620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (0.05, 0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0012 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHCB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.28 (-0.48, -0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0054 **\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.26 (-0.45, -0.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0101 *\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"5\"\u003e\n \u003cp\u003eModel 1: only single direction (positive or negative) POPs indicators.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eModel 2: combined all GHGs indicators for univariate analyzes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eBold font indicates statistical significance at the 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eSignif. codes: 0 \u0026lsquo;***\u0026rsquo; 0.001 \u0026lsquo;**\u0026rsquo; 0.01 \u0026lsquo;*\u0026rsquo; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 DLNM models\u003c/h2\u003e\n \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.1 DLNM models: overall lagging effect\u003c/h2\u003e\n \u003cp\u003eAs shown in \u003cstrong\u003eFigure S2\u003c/strong\u003e, reported malaria cases in China demonstrate a nonlinear lagged association with the organic compounds PCB and HCB, where different lag months correspond to distinct epidemiological effects. Specifically, the lag effect of high-level PCB occurs rapidly but lasts for a short duration (\u003cstrong\u003eFigure S2 A, B\u003c/strong\u003e), whereas the lag effect of low-level HCB occurs slowly (risk emerging after approximately a 1-month lag) but persists for a longer duration (\u003cstrong\u003eFigure S2 C, D\u003c/strong\u003e).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.2 DLNM models: exposure-reaction relationship\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the overall impact of two major persistent organic compounds (PCB and HCB) on the total number of cases for different types of malaria over a 6-month period. Overall, the relative risk of malaria increases with higher PCB levels (above 520T) and decreases with higher HCB levels. For relapsing malaria and unclassified malaria types, the relative risk of malaria increases with higher PCB levels (above 480T) and decreases with higher HCB levels. For malignant malaria, the relative risk of malaria increases with higher HCB levels (above 400T) and decreases with higher PCB levels.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.3 DLNM models: extreme lagging cumulative effect\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reflects the cumulative effects of the organic compounds PCB and HCB over different months (0\u0026ndash;6 months). In overall malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 7.977 to 36,726.46 over the 0\u0026ndash;6 months period. In contrast, extremely low levels of HCB had cumulative effect values of 3.602 (1.103, 11.765) in the first 3 months and 4.749 (1.11, 20.31) in the first 4 months, both of which increase the risk of malaria in the population. Extremely high levels of HCB showed a protective effect during the first 2 months. For relapsing malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 14.058 to 638,534.2 over the 0\u0026ndash;6 months period. Extremely low levels of HCB had cumulative effect values of 3.164 (1.157, 8.651) in the first 2 months and 9.462 (1.087, 82.341) in the first 6 months, both increasing the risk of malaria in the population. Extremely high levels of HCB had a protective effect in the first month. For malignant malaria, there were no significant cumulative effect values, but both extremely low levels of PCB and extremely high levels of HCB may increase the risk of malaria. In unclassified malaria patients, the cumulative lag effect of extremely high levels of PCB increased from 2.83 to 124,535.9 over the 0\u0026ndash;6 months period. Extremely low levels of HCB had cumulative effect values of 2.944 (1.044, 8.3) in the first 3 months and 5.584 (1.113, 28.021) in the first 6 months, both increasing the risk of malaria in the population.\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCumulative effects of organic compounds PCB and HCB in each month (0\u0026ndash;6 months).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eSeries\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCumulative risk (95% CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"4\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elow PCB effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh PCB effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elow HCB effect\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHigh HCB effect\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eTotal malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.034 (0.789, 1.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.977 (2.172, 29.29)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.03 (0.722, 1.469)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.27 (0.09, 0.813)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.849 (0.481, 1.501)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e76.349 (6.97, 836.301)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.558 (0.851, 2.852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.057 (0.009, 0.383)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.526 (0.221, 1.251)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e949.048 (33.266, 27075.09)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4 (0.986, 5.838)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.01 (0.001, 0.156)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.335 (0.103, 1.093)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4687.511 (59.827, 367269.4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.602 (1.103, 11.765)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003 (0, 0.095)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.254 (0.057, 1.135)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e11706.39 (56.906, 2408162)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.749 (1.11, 20.31)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001 (0, 0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249 (0.042, 1.489)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e21793.87 (46.533, 10207277)\u003c/strong\u003e\u003c/p\u003e\n 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\u003cp\u003e\u003cstrong\u003e0.051 (0.004, 0.682)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.294 (0.116, 0.746)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2056.348 (47.445, 89125.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.164 (1.157, 8.651)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.01 (0, 0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.152 (0.041, 0.558)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e20062.42 (149.797, 2686977)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.293 (1.357, 20.646)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004 (0, 1.156)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.095 (0.018, 0.502)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e111089.8 (273.58, 45109043)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.725 (1.417, 42.1)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003 (0, 3.075)\u003c/p\u003e\n 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1198971679)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.462 (1.087, 82.341)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003 (0, 12.814)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eMalignant malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.687 (0.595, 4.782)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (0.001, 15.822)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.954 (0.356, 2.556)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.317 (0.026, 1514.783)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.776 (0.271, 284.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0, 768.387)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.702 (0.029, 17.126)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20128.19 (0, 1.14E\u0026thinsp;+\u0026thinsp;12)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e124.882 (0.116, 134936.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0, 15885.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.243 (0, 212.675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11133181828 (0, 5.49E\u0026thinsp;+\u0026thinsp;25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4202.652 (0.091, 193591131)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0, 1218.606)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.066 (0, 4357.513)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.48E\u0026thinsp;+\u0026thinsp;17 (0, 3.84E\u0026thinsp;+\u0026thinsp;42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e115946.8 (0.087, 1.5422E\u0026thinsp;+\u0026thinsp;11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0, 11.954)\u003c/p\u003e\n \u003c/td\u003e\n 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align=\"left\"\u003e\n \u003cp\u003e2171216 (0.079, 5.97E\u0026thinsp;+\u0026thinsp;13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0, 0.091)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006 (0, 1418617)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.15E\u0026thinsp;+\u0026thinsp;32 (0, 1.57E\u0026thinsp;+\u0026thinsp;73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"7\"\u003e\n \u003cp\u003eUnclassified malaria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.809 (0.653, 1.003)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.83 (1.152, 6.951)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.143 (0.867, 1.508)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.457 (0.198, 1.055)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.462 (0.288, 0.74)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e15.49 (2.497, 96.076)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.365 (0.82, 2.271)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.447 (0.094, 2.119)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.174 (0.081, 0.374)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e323.513 (23.124, 4525.97)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.88 (0.87, 4.064)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.249 (0.024, 2.596)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.078 (0.026, 0.232)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3907.952 (121.723, 125465.6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.944 (1.044, 8.3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.104 (0.005, 2.199)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.045 (0.011, 0.182)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e22471.07 (329.054, 1534548)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.442 (1.24, 15.916)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.046 (0.001, 1.772)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.031 (0.006, 0.168)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e69762.35 (552.311, 8811678)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.627 (1.292, 24.5)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.028 (0, 1.721)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLag0-6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.026 (0.004, 0.176)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e124535.9 (629.074, 24654021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.584 (1.113, 28.021)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.027 (0, 2.234)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eBold font indicates statistical significance at the 0.05 level.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable S7\u003c/strong\u003e reflects the cumulative effects of the organic compounds PCB and HCB across all lag months (6 months). For both overall and relapsing malaria cases, the cumulative lag effects of extremely high PCB levels (P95) over a 0\u0026ndash;6 month period were 3.673e\u0026thinsp;+\u0026thinsp;4 (41.930, 3.217e\u0026thinsp;+\u0026thinsp;7) and 6.385e\u0026thinsp;+\u0026thinsp;5 (340.063, 1.199e\u0026thinsp;+\u0026thinsp;9), respectively, indicating a significant elevation in malaria risk. Additionally, for low levels of HCB (P75-P95), the cumulative lag effects over 0\u0026ndash;6 months were 9.462 (1.087, 82.341) and 2.889 (1.005, 8.310), respectively, both of which increase the risk of developing malaria in relapsing malaria patients. For malignant malaria, high levels of HCB (P75) corresponded to a cumulative lag effect over 0\u0026ndash;6 months of 3.307e\u0026thinsp;+\u0026thinsp;46 (11.764, 9.296e\u0026thinsp;+\u0026thinsp;91), which increases the risk of developing malaria in patients. In unclassified malaria, both high levels of PCB (P75-P95) and low levels of HCB (P5-P25) corresponded to cumulative lag effects greater than 1, which increases the risk of developing malaria in patients.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.4 DLNM models: extreme lagging relative effect\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e reflects the current-month lag effects of the organic compounds PCB and HCB across different months. In both overall malaria and Relapsing malaria types, the lag effect of low-level PCB in the 6th month showed a significant positive risk level. High-level PCB and low-level HCB exhibited significant risk levels in the 1st to 4th months, with the lag effect in the 2nd month showing the highest peak. In malignant malaria, there were no significant single-month lag effects. In unclassified malaria, high-level PCB and low-level HCB showed significant risk levels in the 2nd to 5th months, with the highest peak lag effect observed in the 2nd and 3rd months, respectively.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e\n \u003ch2\u003e3.6.5 DLNM models: sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eBased on the QAIC (Quasi-Akaike Information Criterion) selection criterion, the optimal maximum lag period was determined to be 6 months. According to the minimum partial autocorrelation function (PACF) criterion, the derived parameters exhibited variation across different malaria types. In the overall patient population, the degrees of freedom (df) for the time variable were 2, for PCB was 6, and for HCB was 4. In relapsing malaria patients, the degrees of freedom for the time variable were 2, for PCB was 6, and for HCB was 5. In malignant malaria patients, the degrees of freedom for the time variable were 5, for PCB was 6, and for HCB was 7. In unclassified malaria patients, the degrees of freedom for the time variable were 2, for PCB was 4, and for HCB was 4.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eMalaria in China predominantly occurs during the summer and autumn seasons, driven by warm, humid conditions that favor mosquito breeding and the transmission of Plasmodium pathogens(\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This seasonal pattern aligns with findings from a Yunnan-based study(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Geographically, malaria in China is concentrated in the southwestern and eastern regions, suggesting that climatic factors\u0026mdash;particularly in the southwest (e.g., Yunnan Province), where tropical and subtropical conditions prevail\u0026mdash;play a critical role. High temperatures and humidity accelerate mosquito reproduction, while the rainy season creates stagnant water bodies (e.g., ponds) that serve as ideal breeding sites, thereby increasing malaria transmission risk. Regarding malaria types, Plasmodium vivax (tertian malaria) is the predominant form in China, owing to the widespread distribution of Anopheles mosquitoes, particularly Anopheles sinensis, a highly competent vector for P. vivax(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Currently, the primary source of malignant malaria is imported malaria.\u003c/p\u003e \u003cp\u003eStudies have found that during China\u0026rsquo;s industrialization process, industries such as coal combustion, oil refining, and steel production have emerged. In these sectors, coal, as one of the main energy sources, releases large amounts of polycyclic aromatic hydrocarbons during combustion(\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), especially in regions using incomplete combustion equipment and technologies, where emissions are more severe. This aligns with the findings of the study, which identifies manufacturing and construction industries as the primary sources of China's POPs, followed by biomass burning emissions. The study also found that PCB emissions primarily stem from electricity and thermal power generation, consistent with the findings of a study conducted along the Indus River(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, SARIMA seasonal model is more effective in detecting subtle differences during stable periods, thereby achieving stable predictions. This finding is consistent with previous research results(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The study also applied the Kalman filter model showed superior performance. This is in agreement with the results of Sangil Kim and others in predicting Hand, Foot, and Mouth Disease(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). In the multivariate predictive analysis, the SVM and XGBoost models were found to be more suitable for predicting the impact of POPs on malaria incidence in China, which is supported by similar findings in several studies(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis study revealed that the risk of malaria incidence associated with POPs is substantially amplified under the mediating influence of GHGs. Additionally, an experimental study demonstrating elevated microbial respiration (manifested as increased CO₂ production) observed a corresponding rise in the release of organic pollutants into aquatic systems(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). This might be due to certain greenhouse gases (such as CO\u003csub\u003e2\u003c/sub\u003e) facilitating the release of POPs, thereby enhancing the risk of malaria. In detailed studies, we observed that PAH congeners and PCDD exhibited positive risk effects on the incidence of different malaria types. The most common pollutants in urban environments include heavy metals, PAHs, and others. These pollutants persist in the environment and can accumulate in air, water, and soil, leading to complex interactions affecting urban ecosystems(\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). This finding is consistent with a study conducted in Africa, which revealed that polycyclic aromatic hydrocarbons (PAHs) can drive insecticide resistance in malaria vectors, thereby indirectly elevating the potential risk of malaria transmission and incidence(\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Therefore, aquatic and terrestrial organisms in these ecosystems constantly absorb these substances, which may result in bioaccumulation and impact their behavior and reproduction(\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Due to the key role of mosquitoes in decomposition and the food web, as well as their utility as environmental pollution sentinels, they accumulate organic pollutants in their tissues. These pollutants, such as PAHs, usually affect their adult stages and may influence mosquito reproduction(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). A U.S. study found that direct exposure to dioxin-like organic compounds could progressively increase the susceptibility of separated human red blood cells to Plasmodium falciparum after 48 hours(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This finding is similar to our observation that organic pollutants like PCDD can increase the risk of malaria. The lag analysis showed that long-term PCB exposure was positively associated with increased malaria risk, as previously discussed. Several prior studies have confirmed that compounds like DDT can control malaria transmission by killing pathogen larvae(\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). This is similar to the finding in this study that HCB shows a protective effect on malaria incidence under long-term lag (with a dose-response relationship showing negative feedback).\u003c/p\u003e \u003cp\u003eWhile prior studies have primarily examined how organic pollutants (e.g., DDT) may mitigate malaria transmission, the role of POPs in enhancing transmission has been overlooked. This study addresses this gap by analyzing GHGs and their mediating influence on pollutant impacts. For predictive modeling, we improve upon traditional approaches by integrating the Kalman filter, enabling high-precision forecasts without reliance on fluctuation amplitude. Furthermore, we broke through traditional meteorological and pollution model analysis to explore the lag effects of environmental factors, analyzing the long-term lag effects of organic pollutants on malaria risk. However, due to data limitations, further research is needed on other malaria subtypes. There is some collinearity among various POPs factors; however, these factors were included in the analysis due to their statistical significance, in order to draw reasonable conclusions. This issue of collinearity may be addressed in future environmental health studies using the Qgcomp analysis method. POP emission data do not directly represent human exposure. Ecological exposure estimates may introduce measurement error and ecological bias. Regarding etiological risks, there is a lack of data on pathogens such as Plasmodium, which highlights the need for future scholars to collect pathogen-level data and establish animal and statistical models to investigate the etiological and transmission mechanisms, in order to determine causal relationships.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe analyzed malaria surveillance and early warning in mainland China, along with lagged effects of organic pollutants on malaria incidence. Extreme levels of organic pollutants (PCB, HCB) are associated with increased malaria transmission risk, with varying transmission peaks. Strengthening POPs emission control activities during these periods may help reduce the risk of seasonal malaria susceptibility.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMalaria is classified as a Class B infectious disease under China's Infectious Disease Prevention and Control Law. This study was approved by the Ethics Committee of the Chinese Center for Disease Control and Prevention (CDC). For confidentiality purposes, all data were analyzed anonymously. Due to the mandatory monitoring required by China's Infectious Disease Prevention and Control Law and the public database, patient consent is not required. All data usage strictly complies with the \"Data Security Law of the People's Republic of China\" and the \"Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases.\" Official permission has been obtained from the China CDC. The authorization document clearly specifies that the data usage is limited to aggregated incidence rate statistics and does not involve individual patient privacy information, fully complying with public health data security management regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have consented to publication of this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe disease data was publicly supported from the National Public Health Data Centre of China (https://www.phsciencedata.cn/). Pollutants information was publicly from the Emissions Database for Global Atmospheric Research (EDGAR) (https://edgar.jrc.ec.europa.eu/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the National Public Health Data Centre of China (https://www.phsciencedata.cn/) and the the Emissions Database for Global Atmospheric Research (EDGAR) (https://edgar.jrc.ec.europa.eu/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGuolong Qu:\u003c/strong\u003e Software, Conceptual, Methodology, Formal analysis, Investigation, Resources, Writing-original draft, Writing-review \u0026amp; editing. \u003cstrong\u003eJianqiang Han:\u003c/strong\u003e Software, Conceptual, Methodology, Formal analysis, Investigation, Writing-original draft, Writing-review \u0026amp; editing. \u003cstrong\u003eZhenyao Song:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Writing-review \u0026amp; editing, Supervision. \u003cstrong\u003eWeiming Hou:\u0026nbsp;\u003c/strong\u003eConceptualization, Methodology, Formal analysis, Writing-review \u0026amp; editing, Funding acquisition, Supervision. All authors had full access to the data, contributed to the study, approved the final version for publication, and take responsibility for its accuracy and integrity. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTao ZY, Fang Q, Liu X, Culleton R, Tao L, Xia H, Gao Q. Congenital malaria in China. PLoS Negl Trop Dis. 2014 Mar;8(3):e2622. eng. Epub 2014/03/15. doi:10.1371/journal.pntd.0002622. 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Cited in: Pubmed; PMID 39407740.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Malaria, Persistent Organic Pollutants, PCB, DLNM, Kalman filter","lastPublishedDoi":"10.21203/rs.3.rs-8542332/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8542332/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates malaria incidence trends in mainland China from 2005 to 2020, to elucidate its epidemiological characteristics and investigate potential associations with air pollution. First, time series analysis and machine learning methods were employed to predict malaria incidence. Next, the weighted quantile sum (WQS) model and distributed lag nonlinear model (DLNM) were utilized to assess the risk of malaria linked to persistent organic pollutants (POPs). Seasonal Autoregressive Integrated Moving Average Model (SARIMA) model outperforms the Holt-Winters model in univariate traditional models for malaria, with the optimally configured SARIMA (1,1,0) (1,0,1) [12]. For the new Kalman filter model, showing good results across both overall malaria and individual subtypes (MAE ∈ [0.001, 0.016]). In multivariate prediction, the models with the best performance are Gradient Boosting (XGBoost) and Support Vector Machine (SVM). Risk levels for Polychlorinated Biphenyls (PCB) and Hexachlorobenzene (HCB) were coefficients (95% CI): -1.48 (-2.69, -0.27) and -1.39 (-2.57, -0.22), respectively. Cumulative effect of extremely low-level HCB during the first 3 and 4 months were 3.602 (1.103, 11.765) and 4.749 (1.11, 20.31), respectively, indicating an increased risk of malaria incidence. Our current study not only investigated the spatiotemporal surveillance and early warning systems for malaria incidence in mainland China but also elucidated the lagged exposure-response relationships and potential associations between organic pollutants and malaria occurrence. Strengthening POPs emission control activities during this period may help reduce the risk of seasonal malaria susceptibility.\u003c/p\u003e","manuscriptTitle":"Effect of Persistent Organic Pollutants on malaria in Chinese mainland: a population-based surveillance and modelling study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-09 16:44:14","doi":"10.21203/rs.3.rs-8542332/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6a59ae2-faee-4bbe-a8c3-b3af4dbb1b52","owner":[],"postedDate":"January 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-05T20:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-09 16:44:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8542332","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8542332","identity":"rs-8542332","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}