The Ecological Study on Decadal Trends and Impacts of Ambient Air Pollutants on COPD and Lung Cancer in Upper Northern Thailand: 2013-2022

The Ecological Study on Decadal Trends and Impacts of Ambient Air Pollutants on COPD and Lung Cancer in Upper Northern Thailand: 2013-2022 | 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 The Ecological Study on Decadal Trends and Impacts of Ambient Air Pollutants on COPD and Lung Cancer in Upper Northern Thailand: 2013-2022 Pachara Sapbamrer, Pheerasak Assavanoppkhun, Jinjuta Panumasvivat This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3875948/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 Upper northern Thailand faced a crisis of air pollution, posing significant challenges to respiratory health. This study aimed to investigate the trends and associations between air pollutant levels and lung cancer and chronic obstructive pulmonary disease (COPD). This study spanned eight provinces over 2013–2022, collecting air pollutant monitoring data from the Pollution Control Department and respiratory health information, including mortality rates for lung cancer and COPD patients, along with the readmission rate for COPD patients, from Regional Public Health. The dataset was divided into two seasons, namely, the haze (December-May) and non-haze (June-November) seasons. The findings indicated a decadal pattern, with peak levels observed in March for all air pollutant parameters and COPD readmission rates. The PM2.5 concentration exceeded Thailand's air quality standards from January to April. COPD mortality and readmission rates significantly increased compared to those in the non-haze periods ( p < 0.001). While lung cancer mortality rates were greater in the haze season, the difference was not statistically significant. Pearson correlation analysis indicated moderate positive associations between PM 10 , PM 2.5 , O 3 , CO, and NO 2 levels and COPD readmission rates (r = 0.308 to 0.495, p < 0.01). Moreover, the PM 10 , PM 2.5 , O 3 , SO 2 and NO 2 concentrations exhibited a weak positive association with the COPD mortality rate (r 0.014 to 0.288, p < 0.01). Upper northern Thailand experienced a predictable pattern of air pollution, positively linked to higher COPD death and readmission rates. These findings highlight the need for an early and well-prepared public health response, especially before the haze season. air pollution particulate matter respiratory disease lung cancer chronic obstructive pulmonary disease Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Air pollution is a major global environmental risk to population health. The World Health Organization (WHO) suggests that ninety-nine percent of the global population breathe air that exceeds the WHO guideline limits and the population in low- and middle-income countries suffers from the highest exposures(World Health Organization (WHO), 2022 ). In Thailand, the upper northern region of Thailand is the major area faces a problem of air pollution, particularly particulate matters (PM)(Sukkhum, Lim, Ingviya, & Saelim, 2022 ). Biomass burning climatic conditions and topography are the main causes of air pollution in upper northern Thailand. The open burning of crop residues and forest fires during the dry season from January to April are primary sources of air quality in northern Thailand(Suriyawong et al., 2023 ). Forest fires occurring in neighboring countries can also transport air pollutants across Thailand(Sirimongkonlertkun, 2018 ). Regarding the season pattern, the dry season with low rainfall, low wind speed, and temperature inversion, occurs during November and March every year (Pumijumnong & Wanyaphet, 2006 ). El Niño and La Niña are climate cycles that also have an impact on pollution levels. A study showed that the levels of PM2.5, carbon, and metal components during the haze season in El Niño years were significantly higher than in La Niña years due to differences in climatic conditions and other related meteorological factors (Kraisitnitikul et al., 2024 ). Furthermore, upper northern Thailand has a mountain valley topography (N Auipong & Trivej, 2018 ; Solanki, Macatangay, Sakulsupich, Sonkaew, & Mahapatra, 2019 ). Therefore, when open burning and forest fires occur, certain air pollutants are trapped in the valleys, leading to escalating high concentrations of PM and haze smog during the dry season. Exposure to ambient air pollutants such as PM, ozone (O 3 ), sulfur dioxide (SO 2 ), and nitrogen dioxide (NO 2 ) contributes to various adverse health effects, particularly in the respiratory system (Mabahwi, Leh, & Omar, 2014 ). Previous epidemiological studies suggest that exposure to PM and other gaseous pollutants increases the risk and mortality of respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and lung cancer (Areal, Zhao, Wigmann, Schneider, & Schikowski, 2022 ; Chang et al., 2023 ; Mabahwi et al., 2014 ). Therefore, the present study aimed to investigate the trends and associations between air pollutant levels and respiratory diseases, including the mortality rate of lung cancer and COPD, as well as the COPD re-admission rate during 2013–2022 in eight provinces of upper northern Thailand between the haze and non-haze seasons. 2. Materials and Methods 2.1 Study Design and Data Collection This study was conducted in eight provinces of upper northern Thailand, including Chiang Mai, Lam Phun, Lam Pang, Phrae, Nan, Phayao Chiang Rai, and Mae Hong Son. The ten-year historical data on air pollution and respiratory diseases from 2013–2022 were collected using secondary data reports from the Air Quality Management Bureau, Pollution Control Department website(Pollution Control Department, 2023), and the Health Regional Medical Office 1, Ministry of Public Health website(Health Regional 1, 2023 ). The air pollutant data were collected, including PM 10 -24hours, PM 2.5 -24hours, sulphur dioxide (SO 2 )-1hour, nitrogen dioxide (NO 2 )-1hour, carbon monoxide (CO)-1hour, and Ozone-1hour. The respiratory disease data were collected, including re-admission cases for COPD, death cases from COPD and lung cancer, and the total number of in-patient cases for COPD and lung cancer. The formula for calculating the mortality and re-admission rate was as follows: $$\text{R}\text{e}-\text{a}\text{d}\text{m}\text{i}\text{s}\text{s}\text{i}\text{o}\text{n} \text{r}\text{a}\text{t}\text{e} \text{o}\text{f} \text{C}\text{O}\text{P}\text{D}=\frac{\text{r}\text{e}\text{a}\text{d}\text{m}\text{i}\text{s}\text{s}\text{i}\text{o}\text{n} \text{c}\text{a}\text{s}\text{e}\text{s} \text{f}\text{r}\text{o}\text{m} \text{C}\text{O}\text{P}\text{D} x \text{1,000}}{\text{C}\text{O}\text{P}\text{D} \text{c}\text{a}\text{s}\text{e}\text{s} \text{i}\text{n} \text{t}\text{h}\text{e} \text{I}\text{n}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t} \text{D}\text{e}\text{p}\text{a}\text{r}\text{t}\text{m}\text{e}\text{n}\text{t}}$$ $$\text{M}\text{o}\text{r}\text{t}\text{a}\text{l}\text{i}\text{t}\text{y} \text{r}\text{a}\text{t}\text{e} \text{o}\text{f} \text{C}\text{O}\text{P}\text{D}=\frac{\text{d}\text{e}\text{a}\text{t}\text{h} \text{c}\text{a}\text{s}\text{e}\text{s} \text{f}\text{r}\text{o}\text{m} \text{C}\text{O}\text{P}\text{D} x \text{1,000}}{\text{C}\text{O}\text{P}\text{D} \text{c}\text{a}\text{s}\text{e}\text{s} \text{i}\text{n} \text{t}\text{h}\text{e} \text{I}\text{n}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t} \text{D}\text{e}\text{p}\text{a}\text{r}\text{t}\text{m}\text{e}\text{n}\text{t}}$$ $$\text{M}\text{o}\text{r}\text{t}\text{a}\text{l}\text{i}\text{t}\text{y} \text{r}\text{a}\text{t}\text{e} \text{o}\text{f} \text{l}\text{u}\text{n}\text{g} \text{c}\text{a}\text{n}\text{c}\text{e}\text{r}=\frac{\text{d}\text{e}\text{a}\text{t}\text{h} \text{c}\text{a}\text{s}\text{e}\text{s} \text{f}\text{r}\text{o}\text{m} \text{l}\text{u}\text{n}\text{g} \text{c}\text{a}\text{n}\text{c}\text{e}\text{r} x \text{1,000}}{\text{L}\text{u}\text{n}\text{g} \text{C}\text{a}\text{n}\text{c}\text{e}\text{r} \text{C}\text{a}\text{s}\text{e}\text{s} \text{i}\text{n} \text{t}\text{h}\text{e} \text{I}\text{n}\text{p}\text{a}\text{t}\text{i}\text{e}\text{n}\text{t} \text{D}\text{e}\text{p}\text{a}\text{r}\text{t}\text{m}\text{e}\text{n}\text{t}}$$ The dataset was categorized into two seasons, namely, the haze season (December-May) and the non-haze season (June-November). Data imputation using a regression model was used to retain the majority of the dataset's data by substituting missing data with a different value. An independent t-test was utilized to compare the differences in air pollutant levels, mortality rates of lung cancer and COPD, and re-admission rates of COPD between the haze and the non-haze seasons. Pearson correlation coefficient analysis was used to investigate the associations of air pollutant levels with the mortality rate of lung cancer and COPD, and the re-admission rate of COPD. The significance level was set at p-value < 0.05. 2.2 Ethical Consideration The study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University (Study Code: COM-2566-0249). 3. Results 3.1 Air pollutant levels and respiratory diseases in Upper Northern Thailand during 2013 – 2022 During 2013-2022, an average of 27.53 + 24.08 µg/m 3 was detected for PM 2 . 5 -24 hours and 42.02 + 29.45 µg/m 3 for PM 10 -24 hours was detected. Regarding with gaseous pollutants, the average was 10.9 + 0.92 ppb for SO 2 -1hour, 0.47 + 4.69 ppb for NO 2 -1hour, 0.56 + 0.39 ppm for CO-1hour, and 23.20 + 10.87 ppb for ozone-1hour. Considering with respiratory diseases during 2013-2021, the data showed the the average of lung cancer mortality rate/1,000 was 6.95 + 5.49, whereas 80.54 + 60.54 for COPD mortality rate/1,000, and 16.16 + 4.57 for COPD re-admission rate/1000. Additional air pollutant levels and the prevalence of respiratory diseases in each province are shown in Table1. 3.2 Trend of A ir P ollutant L evels and Respiratory Diseases Classified by month, 2013 - 2022 The monthly air pollutants levels from 2013 - 2022 are presented in Figure 1. The highest levels of PM 2 . 5 -24 hours, PM 10 -24 hours, NO 2 -1hour, CO-1hour, and Ozone-1hour. were detected in March. Meanwhile, the highest levels of SO 2 -1hr.were detected in December. The lowest levels of air pollutants varied from June to September, with most of the lowest level period found in July and August. When comparing PM 2 . 5 and PM 10 levels with WHO and Thai standards, PM 2 . 5 levels during January- May, and November and December exceeded the WHO standard, whereas PM 2 . 5 levels during January and April exceeded the new standard of Thailand. PM 10 levels during January- April exceeded the WHO standard, however, PM 10 levels for all months didn’t exceed the Thai standard. SO 2 , NO 2 , CO, and Ozone levels in all months didn’t exceed the Thai standard. Respiratory diseases classified by month, 2013-2022 are presented in Figure 2. For COPD, the highest mortality rate was found in April with an average of 1.81 per 1,000 populations, whereas the highest re-admission rate was found in March with an average of 20.68 per 1,000 populations. The trend for COPD mortality rate and re-admission rate had an upward trend during January and April. Difference lung cancer, the highest mortality rate was found in August with an average of 8.28 per 1,000 populations, and the trend of mortality rate of lung cancer fluctuated. Table 1 . Air pollutant levels and respiratory diseases in Upper Northern Thailand from 2013 – 2022 Parameters Upper Northern region Provinces Chiang Mai Lam Phun Lam Pang Phrae Nan Phayao Chiang Rai Mae Hong Son Air pollution (duration 2013-2022), Mean + SD PM 2 . 5 -24hrs., ug/m 3 27.53 + 24.08 29.87 + 20.92 26.31 + 16.84 25.60 + 20.02 28.83 + 21.04 23.27 + 20.42 24.92 + 17.97 33.17 + 33.91 28.11 + 33.99 PM 10 -24 hrs., ug/m 3 42.02 + 29.45 44.39 + 25.12 42.25 + 23.76 41.56 + 27.23 44.52 + 26.69 41.10 + 27.78 39.36 + 28.25 43.86 + 33.62 39.26 + 40.05 SO 2 -1hr.(ppb) 10.9 + 0.92 1.05 + 0.42 1.73 + 1.00 1.39 + 0.52 1.33 + 1.03 0.93 + 0.87 1.40 + 1.18 0.87 + 0.34 N/A NO 2 -1hr.(ppb) 0.47 + 4.69 11.75 + 5.60 8.93 + 6.24 4.99 + 2.28 8.00 + 4.07 3.59 + 1.94 5.32 + 2.85 5.29 + 3.22 3.69 + 2.43 CO-1hr.(ppm) 0.56 + 0.39 0.75 + 0.29 0.49 + 0.22 0.64 + 0.23 0.45 + 0.20 0.45 + 0.18 0.42 + 0.19 0.63 + 0.31 0.59 + 0.26 Ozone-1hr.(ppb) 23.20 + 10.87 25.34 + 9.73 25.47 + 10.27 24.94 + 10.90 24.77 + 10.91 21.84 + 9.62 25.11 + 12.58 19.64 + 8.92 18.92 + 10.92 Respiratory outcome (duration 2013-2021), Mean + SD COPD death case 6.68 + 4.94 10.83 + 4.41 3.68 + 2.35 7.48 + 3.77 4.90 + 2.43 8.24 + 3.25 4.98 + 2.84 12.50 + 5.57 1.13 + 1.05 COPD mortality rate/1,000 1.37 + 0.81 0.91 + 0.83 1.55 + 0.94 1.14 + 0.62 1.67 + 0.84 1.90 + 0.74 1.46 + 0.74 1.64 + 0.69 0.74 + 0.68 COPD re-admission case 80.54 + 60.54 189.83 + 41.68 36.54 + 11.26 114.73 + 37.54 45.68 + 17.25 53.79 + 19.01 45.48 + 15.12 131.53 + 37.99 27.51 + 8.76 COPD re-admission rate/1000 16.16 + 4.57 16.48 + 3.18 15.21 + 1.18 18.47 + 3.77 15.82 + 4.64 12.37 + 3.27 14.05 + 3.61 17.78 + 4.35 18.91 + 5.27 Lung cancer death case 4.61 + 3.25 7.86 + 2.98 2.26 + 1.46 5.47 + 2.30 3.11 + 2.06 6.62 + 2.59 3.37 + 1.91 7.28 + 3.13 1.21 + 1.10 Lung cancer mortality rate/1,000 6.95 + 5.49 2.85 + 1.08 5.61 + 3.68 4.31 + 1.81 8.87 + 5.35 10.58 + 4.79 7.38 + 3.56 5.53 + 2.36 10.72 + 9.94 N/A = no data 3.3 Comparison of Air Pollutant Levels and Respiratory Diseases Between Haze and Non - haze seasons Visualization of air pollutants in eight provinces of upper northern Thailand between haze and non-haze seasons are presented in Figure 3. The levels of PM 2 . 5 ,PM 10 and O 3 of all provinces during haze season were higher than non-haze season. The highest PM 2 . 5 levels during haze season was found in Mae Hong Son (50.7 ug/m 3 ), Chiang Rai (46.3 ug/m 3 ), and Chiang Mai (44.0 ug/m 3 ), respectively. However, the levels of other pollutants, which included SO 2 , NO 2 , and CO, among eight provinces were rather similar both in haze and non-haze season. When comparing air pollutant levels and respiratory diseases between haze and non-haze seasons by using independent sample t-test, the results found that the levels of all parameters of air pollutants during haze season were significantly higher than those during non-haze season (P value<0.01). (Table2) Mortality and re-admission rate of COPD of all provinces during haze season were higher than non-haze season. Regarding respiratory diseases, mortality and re-admission rates of COPD during haze season were significantly higher than those during non-haze season (P value<0.01). However, there was no difference in mortality rate of lung cancer during haze and non-haze season (Table 2). Furthur details of respiratory diseases in eight provinces of upper northern Thailand between the haze and non-haze seasons are presented in Figure 4 Table 2 . Comparison of Air Pollutant Levels and Respiratory Diseases between hthe Haze and Non-haze Seasons Haze season Non-haze season Mean difference + SE. (95%CI) P-value + Air pollutants (duration 2013-2022) PM 2 . 5 -24hrs., ug/m 3 42.91 + 25.68 12.14 + 5.20 30.77 + 1.95 (28.42, 33.12) <0.001* PM 10 -24hrs., ug/m 3 61.44 + 30.45 22.59 + 7.27 38.85 + 1.43 (36.05, 41.65) <0.001* SO 2 -1hr.(ppb) 1.18 + 0.95 1.01 + 0.88 0.17 + 0.59 (0.05, 0.28) 0.004* NO 2 -1hr.(ppb) 8.47 + 5.20 4.46 + 3.01 4.00 + 0.27 (3.47, 4.54) <0.001* CO-1hr.(ppm) 0.66 + 0.27 0.47 + 0.45 0.21 + 0.16 (0.18, 0.24) <0.001* Ozone-1hr.(ppb) 30.53 + 10.18 15.88 + 5.02 14.65 + 0.52 (13.64, 15.67) <0.001* Respiratory outcomes (duration 2013-2021) Mortality rate of COPD /1,000 1.59 + 0.84 1.16 + 0.71 0.43 + 0.06 (0.32, 0.54) <0.001* Re-admission rate of COPD /1000 18.29 + 4.55 14.06 + 3.51 4.23 + 0.29 (3.66, 4.79) <0.001* Mortality rate of lung cancer /1,000 7.15 + 5.41 6.74 + 5.57 0.41 + 0.39 (-0.36,1.18) 0.296 + Independent t-test, * p < 0.05 3.4 The Association of Air Pollutant Levels with Mortality Rates of COPD and Lung Cancer, and Re - admission rates of COPD The results found that mortality rate of COPD was weakly positive association with PM 2 . 5 , PM 10 , SO2, NO2, and ozone (P-value <0.01). Re-admission rate of COPD was moderately positive association with PM 2 . 5 , PM 10 , NO2, CO, and ozone (P-value <0.01). Regarding mortality of lung cancer, there were no association with PM 2 . 5 and PM 10 , but there were weakly negative association with SO2 , NO2, and CO. Additional association was showed in Table 3. Table 3 . Pearson Correlation Coefficient ( r ) of Air Pollutant Levels with Mortality Rates of Lung Cancer and COPD, and Re-admission Rates of COPD COPD Lung cancer Mortality rate / 1,000 Re - admission rate / 1000 Mortality rate / 1,000 PM 2 . 5 -24 hrs., ug/m 3 0.233** 0.446** -0.009 PM 10 -24 hrs., ug/m 3 0.288** 0.495** -0.004 SO 2 -1hr.(ppb) 0.136** -0.048 -0.133** NO 2 -1hr.(ppb) 0.171** 0.315** -0.149** CO-1hr.(ppm) 0.035 0.308** -0.098** Ozone-1hr.(ppb) 0.257** 0.343** -0.051 + Pearson correlation; **P value <0.01 4. Discussion The air pollution in the northern part of Thailand followed a predictable seasonal pattern, occurring consistently throughout the same period each year (Punsompong & Chantara, 2018 ; Suriyawong et al., 2023 ). The causes of air pollution in this region involve a multitude of aspects, including pollution sources, weather conditions, and atmospheric conditions. The chemical composition of PM2.5 might change depending on meteorological circumstances and emission sources, resulting in varied adverse health effects in different regions (Bran et al., 2022 ). When reviewing the provincial level, it was discovered that the problem is particularly noticeable in Mae Hong Son and Chiang Rai. Both provinces encounter border-related issues, suggesting a cross-border influence (Engling et al., 2011 ; Punsompong, Pani, Wang, & Bich Pham, 2021 ). In June 2023, Thailand established an updated acceptable PM 2.5 standard, which, however, remains higher than the level set by the World Health Organization. The existing WHO guidelines indicate that the average yearly levels of PM 2.5 should not surpass 5 µg/m 3 . Additionally, the average daily exposure should not exceed 15 µg/m 3 for more than three to four days per year(World Health Organization (WHO), 2021 ) (World Health Organization (WHO), 2021 ). However, Thailand's recent announcement stated that the annual average concentrations of PM 2.5 should not exceed 15 µg/m 3 , and the 24-hour average exposures should not exceed 37.5 µg/m 3 (Pollution Control Department (PCD), 2023 ). It suggests that the previous efforts to manage air pollution in this area were not efficient. Hence, it may be important to implement enhanced measures to deal with the root cause of pollution. COPD are known for acute onset exacerbated by air pollutants. The study revealed a correlation between COPD and ambient air pollution levels. Exposure to pollutants were notably associated with increased hospital readmissions and mortality in COPD cases, and the trend of COPD cases aligned with the levels of air pollutants. Research conducted globally consistently indicates that air pollution significantly influences respiratory diseases in both short-term (Gao, Wang, W, Zhao, & Xia, 2020; Han, Pak, Lee, & Chung, 2022 ; Orellano, Reynoso, Quaranta, Bardach, & Ciapponi, 2020 ) and long-term exposures(Andersen et al., 2012 ; Beelen et al., 2014 ), leading to increased hospital admissions and mortality rates. Earlier research revealed a link between COPD hospitalizations and air pollution levels, especially concerning PM2.5, PM10, O3, and NO(Andersen et al., 2012 ; Gao et al., 2020 ; Han et al., 2022 ). Exposure to PM10 and PM2.5 has been positively linked to an increased risk of respiratory mortality, ranging from 1.0073 to 1.023 (Areal, Zhao, Wigmann, Schneider, & Schikowski, 2022 ; Orellano et al., 2020 ). Findings from a multicenter cohort project study on long-term exposure revealed that even exposure to concentrations less than 20 µg/m 3 led to a rise in mortality rates (Beelen et al., 2014 ). In line with our research, studies conducted in northern Thailand have revealed associations between PM2.5, PM10, and O3 with COPD(Varapongpisan, Frank, & Ingsrisawang, 2022 ), leading to increased acute exacerbations and visits to the emergency room(Pothirat et al., 2019 ; Surit, Wongtanasarasin, Boonnag, & Wittayachamnankul, 2023 ). The increased rates of COPD readmission and mortality may be attributed to the impact of PM on pulmonary function (Bloemsma, Hoek, & Smit, 2016 ; Edginton, O'Sullivan, King, & Lougheed, 2019 ), specifically focusing on forced expiratory volume in one second (FEV1), a primary indicator of COPD mortality. As a result, heightened exposure to PM2.5 which decreases FEV1 values, results in adverse outcomes, contributing to increased hospitalization and mortality. During haze, there was an observed increase in the mortality rate for lung cancer; nevertheless, a statistically significant difference from the non-haze season was not found. Contrary to findings in other Thai studies, previous research indicated that the population-attributable fraction of PM2.5 for lung cancer was 16.8% in the Thai population (Pinichka et al., 2017 ). Additionally, the reported number of deaths from lung cancer attributed to PM2.5 and PM10 in Northern Thailand was approximately 0.04% and 0.06%, respectively(Supasri, Gheewala, Macatangay, Chakpor, & Sedpho, 2023 ). This could be clarified by considering that lung cancer has a prolonged period of exposure leading to fatalities. Cumulative doses may accumulate over time before ultimately resulting in death. Based on a study conducted in the U.S.(Pun, Kazemiparkouhi, Manjourides, & Suh, 2017 ), the risk of lung cancer mortality increased from 1.13 to 1.33 times when transitioning from a 12-month moving average PM2.5 exposure to a 60-month moving average exposure. This study exclusively analyzed year-by-year mortality rates without considering cumulative effects, latency periods, or lag times. This study demonstrated the predictable pattern of seasonal haze, which consistently exhibited an upward trend from January to April annually. These robust findings call for decisive action from stakeholders, including governmental bodies, environmental agencies, public health officials, and healthcare providers. Proactive measures are crucial for ensuring a well-prepared and timely public health response in anticipation of the upcoming haze season. The findings reveal significant health impacts of air pollution on respiratory diseases. Consequently, it becomes imperative for authorities to thoroughly investigate the root causes of the pollution problems. Implementing policies or legal measures to target these root causes can pave the way for a more effective and lasting solution. This study has a distinct strength in its focus on northern Thailand, an area globally recognized for its severe air pollution issues. Utilizing a decade-long dataset from a reliable standard source contributes to the robustness of our findings. The visualization of our data is not only comprehensive and easily interpretable, offering valuable insights that can be seamlessly integrated into evidence-based decision-making processes, and public health initiatives. However, this study has a several limitations. Firstly, the study focused on the individual impact of each pollutant on the outcomes, potentially missing synergistic effects on the respiratory system, where multiple pollutants may influence the same organ outcomes. Second, the study used a year-by-year cross-sectional analysis, which may not fully account for latency periods or lag times. Lastly, constrained by the limitations of the health data reporting system, the study lacked comprehensive information such as specific population.s Sub-analyses within specific groups, such as gender and age, were not conducted, potentially preventing the identification of distinct results. In future studies, it is advisable to adopt a comprehensive approach by considering the combined impact of multiple pollutants on outcomes. Utilizing cohort designs, moving average calculation, or advanced time series analysis methods can enhance the understanding of associations. Given the evident health impacts of air pollution and the predictability of seasonal haze, studies on reducing re-admissions and mortality rate are recommended. 5. Conclusions The seasonal haze in northern Thailand follows a consistent trend from January to April, presenting a predictable pattern. The study established an association between air pollution, specifically PM2.5, PM10, and O3, with the mortality and re-admission rates of COPD. These findings emphasize the importance of an early and well-prepared public health response, especially before the haze season. Recommending the implementation of policies or legal measures to target the root causes of air pollution emerges as a crucial step toward a more effective and lasting solution. Declarations Ethics approval and consent to participate This study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University (Study Code : COM-2566-0249). Consent for publication Not applicable. Availability of data and materials The air pollution data are available as open data via the Pollution Control Department online data repository: http://air4thai.pcd.go.th, The anonymised data collected are available as open data via the Health Region 1,Ministry of Public Health online data repository (in Thai) : https://cmi.ciorh1.com/web/index.php Conflict of interest statement The authors declare that they have no competing interests Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions Pachara Sapbamrer: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review & editing.; Pheerasak Assavanoppkhun: Conceptualization, Data curation, Methodology, Validation, Writing - original draft, Writing - review & editing.; Jinjuta Panumasvavat: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing - original draft, Writing - review & editing. All authors read and approved the final manuscript. Acknowledgements Not applicable. Declaration of generative AI in scientific writing During the preparation of this work the author(s) used ChatGPT in order to improve readability and language of the work. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication. References Andersen, Z. J., Bonnelykke, K., Hvidberg, M., Jensen, S. S., Ketzel, M., Loft, S., . . . Raaschou-Nielsen, O. (2012). Long-term exposure to air pollution and asthma hospitalisations in older adults: a cohort study. Thorax, 67 (1), 6-11. https://doi.org/10.1136/thoraxjnl-2011-200711 Areal, A. T., Zhao, Q., Wigmann, C., Schneider, A., & Schikowski, T. (2022). The effect of air pollution when modified by temperature on respiratory health outcomes: A systematic review and meta-analysis. Sci Total Environ, 811 , 152336. https://doi.org/10.1016/j.scitotenv.2021.152336 Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. J., Weinmayr, G., Hoffmann, B., . . . Hoek, G. (2014). Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet, 383 (9919), 785-795. https://doi.org/10.1016/S0140-6736(13)62158-3 Bloemsma, L. D., Hoek, G., & Smit, L. A. M. (2016). Panel studies of air pollution in patients with COPD: Systematic review and meta-analysis. Environ Res, 151 , 458-468. https://doi.org/10.1016/j.envres.2016.08.018 Bran, S. H., Macatangay, R., Surapipith, V., Chotamonsak, C., Chantara, S., Han, Z., & Li, J. (2022). Surface PM2.5 mass concentrations during the dry season over northern Thailand: Sensitivity to model aerosol chemical schemes and the effects on regional meteorology. Atmospheric Research, 277 . Chang, J. H., Lee, Y. L., Chang, L. T., Chang, T. Y., Hsiao, T. C., Chung, K. F., . . . Chuang, H. C. (2023). Climate change, air quality, and respiratory health: a focus on particle deposition in the lungs. Ann Med, 55 (2), 2264881. https://doi.org/10.1080/07853890.2023.2264881 https://doi.org/10.1016/j.atmosres.2022.106303 Edginton, S., O'Sullivan, D. E., King, W., & Lougheed, M. D. (2019). Effect of outdoor particulate air pollution on FEV(1) in healthy adults: a systematic review and meta-analysis. Occup Environ Med, 76 (8), 583-591. https://doi.org/10.1136/oemed-2018-105420 Engling, G., Zhang, Y.-N., Chan, C.-Y., Sang, X.-F., Lin, M., Ho, K.-F., . . . Lee, J. J. (2011). Characterization and sources of aerosol particles over the southeastern Tibetan Plateau during the Southeast Asia biomass-burning season. Tellus B: Chemical and Physical Meteorology, 63 (1). https://doi.org/10.1111/j.1600-0889.2010.00512.x Gao, H., Wang, K., W, W. A., Zhao, W., & Xia, Z. L. (2020). A Systematic Review and Meta-Analysis of Short-Term Ambient Ozone Exposure and COPD Hospitalizations. Int J Environ Res Public Health, 17 (6). https://doi.org/10.3390/ijerph17062130 Han, C. H., Pak, H., Lee, J. M., & Chung, J. H. (2022). : Short-term effects of exposure to particulate matter on hospital admissions for asthma and chronic obstructive pulmonary disease. Medicine (Baltimore), 101 (35), e30165. https://doi.org/10.1097/MD.0000000000030165 Health Regional 1. (2023). Case mix index (CMI) Data Reporting System. Retrieved 1 June 2023, from Ministry of Public Health https://cmi.ciorh1.com/web/ Kraisitnitikul, P., Thepnuan, D., Chansuebsri, S., Yabueng, N., Wiriya, W., Saksakulkrai, S., . . . Chantara, S. (2024). Contrasting compositions of PM(2.5) in Northern Thailand during La Nina (2017) and El Nino (2019) years. J Environ Sci (China), 135 , 585-599. https://doi.org/10.1016/j.jes.2022.09.026 Mabahwi, N. A. B., Leh, O. L. H., & Omar, D. (2014). Human Health and Wellbeing: Human Health Effect of Air Pollution. Procedia - Social and Behavioral Sciences, 153 , 221-229. https://doi.org/10.1016/j.sbspro.2014.10.056 N Auipong, & Trivej, P. (2018). Study of Z-R relationship among different topographies in Northern Thailand. Journal of Physics: Conference Series, 1144 , 012098. https://doi.org/10.1088/1742-6596/1144/1/012098 Orellano, P., Reynoso, J., Quaranta, N., Bardach, A., & Ciapponi, A. (2020). Short-term exposure to particulate matter (PM(10) and PM(2.5)), nitrogen dioxide (NO(2)), and ozone (O(3)) and all-cause and cause-specific mortality: Systematic review and meta-analysis. Environ Int, 142 , 105876. https://doi.org/10.1016/j.envint.2020.105876 Pinichka, C., Makka, N., Sukkumnoed, D., Chariyalertsak, S., Inchai, P., & Bundhamcharoen, K. (2017). Burden of disease attributed to ambient air pollution in Thailand: A GIS-based approach. PLoS One, 12 (12), e0189909. https://doi.org/10.1371/journal.pone.0189909 Pollution Control Department (PCD). (2023). Pollution Control Department Announcement : Air quality index of Thailand, B.E. 2566 . Pothirat, C., Chaiwong, W., Liwsrisakun, C., Bumroongkit, C., Deesomchok, A., Theerakittikul, T., . . . Phetsuk, N. (2019). Acute effects of air pollutants on daily mortality and hospitalizations due to cardiovascular and respiratory diseases. J Thorac Dis, 11 (7), 3070-3083. https://doi.org/10.21037/jtd.2019.07.37 Pumijumnong, N., & Wanyaphet, T. (2006). Seasonal cambial activity and tree-ring formation of Pinus merkusii and Pinus kesiya in Northern Thailand in dependence on climate. Forest Ecology and Management, 226 (1-3), 279-289. https://doi.org/10.1016/j.foreco.2006.01.040 Pun, V. C., Kazemiparkouhi, F., Manjourides, J., & Suh, H. H. (2017). Long-Term PM2.5 Exposure and Respiratory, Cancer, and Cardiovascular Mortality in Older US Adults. Am J Epidemiol, 186 (8), 961-969. https://doi.org/10.1093/aje/kwx166 Punsompong, P., & Chantara, S. (2018). Identification of potential sources of PM10 pollution from biomass burning in northern Thailand using statistical analysis of trajectories. Atmospheric Pollution Research, 9 (6), 1038-1051. https://doi.org/10.1016/j.apr.2018.04.003 Punsompong, P., Pani, S. K., Wang, S.-H., & Bich Pham, T. T. (2021). Assessment of biomass-burning types and transport over Thailand and the associated health risks. Atmospheric Environment, 247 . https://doi.org/10.1016/j.atmosenv.2020.118176 Sirimongkonlertkun, N. (2018). Assessment of Long-range Transport Contribution on Haze Episode in Northern Thailand, Laos and Myanmar. IOP Conf. Ser.: Earth Environ. Sci, 151 , 012017. https://doi.org/10.1088/1755-1315/151/1/012017 Solanki, R., Macatangay, R., Sakulsupich, V., Sonkaew, T., & Mahapatra, P. S. (2019). Mixing Layer Height Retrievals From MiniMPL Measurements in the Chiang Mai Valley: Implications for Particulate Matter Pollution. Frontiers in Earth Science, 7 . https://doi.org/10.3389/feart.2019.00308 Sukkhum, S., Lim, A., Ingviya, T., & Saelim, R. (2022). Seasonal Patterns and Trends of Air Pollution in the Upper Northern Thailand from 2004 to 2018. Aerosol Air Qual. Res, 22 (5), 210318. https://doi.org/https://doi.org/10.4209/aaqr.210318 Supasri, T., Gheewala, S. H., Macatangay, R., Chakpor, A., & Sedpho, S. (2023). Association between ambient air particulate matter and human health impacts in northern Thailand. Sci Rep, 13 (1), 12753. https://doi.org/10.1038/s41598-023-39930-9 Surit, P., Wongtanasarasin, W., Boonnag, C., & Wittayachamnankul, B. (2023). Association between air quality index and effects on emergency department visits for acute respiratory and cardiovascular diseases. PLoS One, 18 (11), e0294107. https://doi.org/10.1371/journal.pone.0294107 Suriyawong, P., Chuetor, S., Samae, H., Piriyakarnsakul, S., Amin, M., Furuuchi, M., . . . Phairuang, W. (2023). Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options. Heliyon, 9 (3), e14261. https://doi.org/10.1016/j.heliyon.2023.e14261 Varapongpisan, T., Frank, T. D., & Ingsrisawang, L. (2022). Association between out-patient visits and air pollution in Chiang Mai, Thailand: Lessons from a unique situation involving a large data set showing high seasonal levels of air pollution. PLoS One, 17 (8), e0272995. https://doi.org/10.1371/journal.pone.0272995 World Health Organization (WHO). (2021). In WHO global air quality guidelines: Particulate matter (PM(2.5) and PM(10)), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide . Geneva. World Health Organization (WHO). (2022). Billions of people still breathe unhealthy air: new WHO data . Retrieved from Geneva, Switzerland: https://www.who.int/news/item/04-04-2022-billions-of-people-still-breathe-unhealthy-air-new-who-data Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3875948","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":273777736,"identity":"f8e0c2b4-a433-409b-a847-ef2889bba7d6","order_by":0,"name":"Pachara Sapbamrer","email":"","orcid":"","institution":"Montfort College, Chiang Mai, Thailand","correspondingAuthor":false,"prefix":"","firstName":"Pachara","middleName":"","lastName":"Sapbamrer","suffix":""},{"id":273777737,"identity":"2aa84630-01a5-4ba6-a6a4-4f672b3eefbb","order_by":1,"name":"Pheerasak Assavanoppkhun","email":"","orcid":"","institution":"Department of Community Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand","correspondingAuthor":false,"prefix":"","firstName":"Pheerasak","middleName":"","lastName":"Assavanoppkhun","suffix":""},{"id":273777738,"identity":"06d25aa5-65d8-4b0f-90dc-4e9854881f0c","order_by":2,"name":"Jinjuta Panumasvivat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACxmYGBgMwi70BJsZDjJYEkMIDUCE2AlogAKRFIoFILcztvAeKeX/YMZhLPn66mYfBTp5BvvcAXi2MzXwJxjwJyQyWs9PMbvMwJBs2sPElENDCYwDUwsxgcDuHDaiFOQHoMANitNQzGNw8A9JST7SWwwwGN3hAWg4Tp8VwTtpxHsueNLObcwyOG7ax5eDXYth/xszgjU21nDn74Wc33lRUy/MznyGgpYGBDaQC6hggyYZXPRDIA6PmAVTxKBgFo2AUjALsAACRpTVNiTG0owAAAABJRU5ErkJggg==","orcid":"","institution":"Department of Community Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand","correspondingAuthor":true,"prefix":"","firstName":"Jinjuta","middleName":"","lastName":"Panumasvivat","suffix":""}],"badges":[],"createdAt":"2024-01-18 13:44:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3875948/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3875948/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51446948,"identity":"af95f961-b60a-4296-b27e-b6d5c8439b6f","added_by":"auto","created_at":"2024-02-21 18:18:16","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1455719,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of Air pollution Levels Classified by Month, 2013-2022\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3875948/v1/dc40871db9954ccb2f39ed80.png"},{"id":51446955,"identity":"0aec37ca-fda6-4c3f-aa8e-4c61ae6f1a67","added_by":"auto","created_at":"2024-02-21 18:18:21","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":782400,"visible":true,"origin":"","legend":"\u003cp\u003eTrend of Respiratory Disease Classified by Month, 2013-2022\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3875948/v1/91a8136da8f46429c126d990.png"},{"id":51446957,"identity":"a177a820-8e06-4113-adea-f221c7b28425","added_by":"auto","created_at":"2024-02-21 18:18:21","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10489010,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of Air Pollutants in Eight Provinces of Upper Northern Thailand between the Haze and Non-haze seasons, during 2013-2022\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3875948/v1/581c63147816f6ba65b0babb.png"},{"id":51446952,"identity":"58d137c8-2442-450f-ba5c-d5e185c477ec","added_by":"auto","created_at":"2024-02-21 18:18:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3424468,"visible":true,"origin":"","legend":"\u003cp\u003eVisualization of Respiratory Diseases in Eight Provinces of Upper Northern Thailand between the Haze and Non-haze seasons, during 2013-2022\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3875948/v1/3e9eae88e117ed2e2528aed9.png"},{"id":53307925,"identity":"7d594395-344f-4d07-aa68-219d1b62926a","added_by":"auto","created_at":"2024-03-23 13:23:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1369011,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3875948/v1/901397aa-28a0-463a-ba29-de7786dc361e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Ecological Study on Decadal Trends and Impacts of Ambient Air Pollutants on COPD and Lung Cancer in Upper Northern Thailand: 2013-2022","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAir pollution is a major global environmental risk to population health. The World Health Organization (WHO) suggests that ninety-nine percent of the global population breathe air that exceeds the WHO guideline limits and the population in low- and middle-income countries suffers from the highest exposures(World Health Organization (WHO), \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In Thailand, the upper northern region of Thailand is the major area faces a problem of air pollution, particularly particulate matters (PM)(Sukkhum, Lim, Ingviya, \u0026amp; Saelim, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Biomass burning climatic conditions and topography are the main causes of air pollution in upper northern Thailand. The open burning of crop residues and forest fires during the dry season from January to April are primary sources of air quality in northern Thailand(Suriyawong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Forest fires occurring in neighboring countries can also transport air pollutants across Thailand(Sirimongkonlertkun, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Regarding the season pattern, the dry season with low rainfall, low wind speed, and temperature inversion, occurs during November and March every year (Pumijumnong \u0026amp; Wanyaphet, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). El Ni\u0026ntilde;o and La Ni\u0026ntilde;a are climate cycles that also have an impact on pollution levels. A study showed that the levels of PM2.5, carbon, and metal components during the haze season in El Ni\u0026ntilde;o years were significantly higher than in La Ni\u0026ntilde;a years due to differences in climatic conditions and other related meteorological factors (Kraisitnitikul et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Furthermore, upper northern Thailand has a mountain valley topography (N Auipong \u0026amp; Trivej, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Solanki, Macatangay, Sakulsupich, Sonkaew, \u0026amp; Mahapatra, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Therefore, when open burning and forest fires occur, certain air pollutants are trapped in the valleys, leading to escalating high concentrations of PM and haze smog during the dry season.\u003c/p\u003e \u003cp\u003eExposure to ambient air pollutants such as PM, ozone (O\u003csub\u003e3\u003c/sub\u003e), sulfur dioxide (SO\u003csub\u003e2\u003c/sub\u003e), and nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e) contributes to various adverse health effects, particularly in the respiratory system (Mabahwi, Leh, \u0026amp; Omar, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Previous epidemiological studies suggest that exposure to PM and other gaseous pollutants increases the risk and mortality of respiratory diseases, such as chronic obstructive pulmonary disease (COPD) and lung cancer (Areal, Zhao, Wigmann, Schneider, \u0026amp; Schikowski, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Chang et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Mabahwi et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Therefore, the present study aimed to investigate the trends and associations between air pollutant levels and respiratory diseases, including the mortality rate of lung cancer and COPD, as well as the COPD re-admission rate during 2013\u0026ndash;2022 in eight provinces of upper northern Thailand between the haze and non-haze seasons.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design and Data Collection\u003c/h2\u003e \u003cp\u003eThis study was conducted in eight provinces of upper northern Thailand, including Chiang Mai, Lam Phun, Lam Pang, Phrae, Nan, Phayao Chiang Rai, and Mae Hong Son. The ten-year historical data on air pollution and respiratory diseases from 2013\u0026ndash;2022 were collected using secondary data reports from the Air Quality Management Bureau, Pollution Control Department website(Pollution Control Department, 2023), and the Health Regional Medical Office 1, Ministry of Public Health website(Health Regional 1, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The air pollutant data were collected, including PM\u003csub\u003e10\u003c/sub\u003e-24hours, PM\u003csub\u003e2.5\u003c/sub\u003e-24hours, sulphur dioxide (SO\u003csub\u003e2\u003c/sub\u003e)-1hour, nitrogen dioxide (NO\u003csub\u003e2\u003c/sub\u003e)-1hour, carbon monoxide (CO)-1hour, and Ozone-1hour. The respiratory disease data were collected, including re-admission cases for COPD, death cases from COPD and lung cancer, and the total number of in-patient cases for COPD and lung cancer. The formula for calculating the mortality and re-admission rate was as follows:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{e}-\\text{a}\\text{d}\\text{m}\\text{i}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n} \\text{r}\\text{a}\\text{t}\\text{e} \\text{o}\\text{f} \\text{C}\\text{O}\\text{P}\\text{D}=\\frac{\\text{r}\\text{e}\\text{a}\\text{d}\\text{m}\\text{i}\\text{s}\\text{s}\\text{i}\\text{o}\\text{n} \\text{c}\\text{a}\\text{s}\\text{e}\\text{s} \\text{f}\\text{r}\\text{o}\\text{m} \\text{C}\\text{O}\\text{P}\\text{D} x \\text{1,000}}{\\text{C}\\text{O}\\text{P}\\text{D} \\text{c}\\text{a}\\text{s}\\text{e}\\text{s} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{I}\\text{n}\\text{p}\\text{a}\\text{t}\\text{i}\\text{e}\\text{n}\\text{t} \\text{D}\\text{e}\\text{p}\\text{a}\\text{r}\\text{t}\\text{m}\\text{e}\\text{n}\\text{t}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\text{M}\\text{o}\\text{r}\\text{t}\\text{a}\\text{l}\\text{i}\\text{t}\\text{y} \\text{r}\\text{a}\\text{t}\\text{e} \\text{o}\\text{f} \\text{C}\\text{O}\\text{P}\\text{D}=\\frac{\\text{d}\\text{e}\\text{a}\\text{t}\\text{h} \\text{c}\\text{a}\\text{s}\\text{e}\\text{s} \\text{f}\\text{r}\\text{o}\\text{m} \\text{C}\\text{O}\\text{P}\\text{D} x \\text{1,000}}{\\text{C}\\text{O}\\text{P}\\text{D} \\text{c}\\text{a}\\text{s}\\text{e}\\text{s} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{I}\\text{n}\\text{p}\\text{a}\\text{t}\\text{i}\\text{e}\\text{n}\\text{t} \\text{D}\\text{e}\\text{p}\\text{a}\\text{r}\\text{t}\\text{m}\\text{e}\\text{n}\\text{t}}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\text{M}\\text{o}\\text{r}\\text{t}\\text{a}\\text{l}\\text{i}\\text{t}\\text{y} \\text{r}\\text{a}\\text{t}\\text{e} \\text{o}\\text{f} \\text{l}\\text{u}\\text{n}\\text{g} \\text{c}\\text{a}\\text{n}\\text{c}\\text{e}\\text{r}=\\frac{\\text{d}\\text{e}\\text{a}\\text{t}\\text{h} \\text{c}\\text{a}\\text{s}\\text{e}\\text{s} \\text{f}\\text{r}\\text{o}\\text{m} \\text{l}\\text{u}\\text{n}\\text{g} \\text{c}\\text{a}\\text{n}\\text{c}\\text{e}\\text{r} x \\text{1,000}}{\\text{L}\\text{u}\\text{n}\\text{g} \\text{C}\\text{a}\\text{n}\\text{c}\\text{e}\\text{r} \\text{C}\\text{a}\\text{s}\\text{e}\\text{s} \\text{i}\\text{n} \\text{t}\\text{h}\\text{e} \\text{I}\\text{n}\\text{p}\\text{a}\\text{t}\\text{i}\\text{e}\\text{n}\\text{t} \\text{D}\\text{e}\\text{p}\\text{a}\\text{r}\\text{t}\\text{m}\\text{e}\\text{n}\\text{t}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe dataset was categorized into two seasons, namely, the haze season (December-May) and the non-haze season (June-November). Data imputation using a regression model was used to retain the majority of the dataset's data by substituting missing data with a different value. An independent t-test was utilized to compare the differences in air pollutant levels, mortality rates of lung cancer and COPD, and re-admission rates of COPD between the haze and the non-haze seasons. Pearson correlation coefficient analysis was used to investigate the associations of air pollutant levels with the mortality rate of lung cancer and COPD, and the re-admission rate of COPD. The significance level was set at p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ethical Consideration\u003c/h2\u003e \u003cp\u003eThe study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University (Study Code: COM-2566-0249).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Air pollutant levels and respiratory diseases in Upper Northern Thailand during 2013\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026ndash;\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring 2013-2022, an average of 27.53\u003cu\u003e+\u003c/u\u003e24.08\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e was detected for PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e-24 hours\u0026nbsp;and 42.02\u003cu\u003e+\u003c/u\u003e29.45\u0026nbsp;\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for PM\u003csub\u003e10\u003c/sub\u003e -24 hours was detected. Regarding with gaseous pollutants, the average was 10.9\u003cu\u003e+\u003c/u\u003e0.92 ppb for SO\u003csub\u003e2\u003c/sub\u003e-1hour, 0.47\u003cu\u003e+\u003c/u\u003e4.69 ppb for NO\u003csub\u003e2\u003c/sub\u003e-1hour, 0.56\u003cu\u003e+\u003c/u\u003e0.39 ppm for CO-1hour, and 23.20\u003cu\u003e+\u003c/u\u003e10.87 ppb for ozone-1hour.\u0026nbsp;Considering with respiratory diseases during 2013-2021, the data showed\u0026nbsp;the\u0026nbsp;the average of lung cancer mortality rate/1,000 was 6.95\u003cu\u003e+\u003c/u\u003e5.49, whereas 80.54\u003cu\u003e+\u003c/u\u003e60.54 for COPD mortality rate/1,000, and 16.16\u003cu\u003e+\u003c/u\u003e4.57 for COPD re-admission rate/1000.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAdditional air pollutant levels and the prevalence of respiratory diseases in each province are shown in Table1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Trend of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003cstrong\u003eir\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eP\u003c/strong\u003e\u003cstrong\u003eollutant\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eL\u003c/strong\u003e\u003cstrong\u003eevels and Respiratory Diseases Classified by month, 2013\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003e2022\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe monthly air pollutants levels from 2013 - 2022\u0026nbsp;are presented in Figure 1.\u0026nbsp;The highest levels of PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e-24 hours, PM\u003csub\u003e10\u003c/sub\u003e-24 hours,\u0026nbsp;NO\u003csub\u003e2\u003c/sub\u003e-1hour,\u0026nbsp;CO-1hour, and\u0026nbsp;Ozone-1hour.\u0026nbsp;were detected in March. Meanwhile, the highest levels of SO\u003csub\u003e2\u003c/sub\u003e-1hr.were detected in December.\u0026nbsp;The lowest levels of air pollutants varied from June to September, with most of the lowest level period found in July and August.\u0026nbsp;When comparing PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u0026nbsp;\u003c/sub\u003eand PM\u003csub\u003e10\u0026nbsp;\u003c/sub\u003elevels with WHO and Thai standards, PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u0026nbsp;\u003c/sub\u003elevels during January-\u0026nbsp;May, and November and December exceeded the WHO\u0026nbsp;standard,\u0026nbsp;whereas PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u0026nbsp;\u003c/sub\u003elevels during January and April exceeded the new standard of Thailand.\u0026nbsp;PM\u003csub\u003e10\u003c/sub\u003e levels during January- April exceeded the WHO standard, however, PM\u003csub\u003e10\u003c/sub\u003e levels for all months didn\u0026rsquo;t exceed the Thai standard. SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, CO, and Ozone levels in all months didn\u0026rsquo;t exceed the Thai standard.\u003c/p\u003e\n\u003cp\u003eRespiratory diseases classified by month, 2013-2022 are presented in Figure 2. For COPD, the highest mortality rate was found in April with an average of 1.81 per 1,000 populations, whereas the highest re-admission rate was found in March with an average of 20.68 per 1,000 populations. The trend for COPD mortality rate and re-admission rate had an upward trend during January and April. Difference lung cancer, the highest mortality rate was found in August with an average of 8.28 per 1,000 populations, and the trend of mortality rate of lung cancer fluctuated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Air pollutant levels and respiratory diseases in Upper Northern Thailand from 2013 \u0026ndash; 2022\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"98%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.367346938775512%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.16326530612245%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eUpper Northern region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.46938775510205%\" colspan=\"8\" valign=\"top\"\u003e\n \u003cp\u003eProvinces\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003eChiang Mai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003eLam Phun\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003eLam Pang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.428571428571429%\" valign=\"top\"\u003e\n \u003cp\u003ePhrae\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003eNan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003ePhayao\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003eChiang Rai\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.857142857142858%\" valign=\"top\"\u003e\n \u003cp\u003eMae Hong Son\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003eAir pollution (duration 2013-2022), Mean \u003cu\u003e+\u003c/u\u003e SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e-24hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e27.53\u003cu\u003e+\u003c/u\u003e24.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e29.87\u003cu\u003e+\u003c/u\u003e20.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e26.31\u003cu\u003e+\u003c/u\u003e16.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e25.60\u003cu\u003e+\u003c/u\u003e20.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e28.83\u003cu\u003e+\u003c/u\u003e21.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e23.27\u003cu\u003e+\u003c/u\u003e20.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e24.92\u003cu\u003e+\u003c/u\u003e17.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e33.17\u003cu\u003e+\u003c/u\u003e33.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e28.11\u003cu\u003e+\u003c/u\u003e33.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;PM\u003csub\u003e10\u003c/sub\u003e-24 hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e42.02\u003cu\u003e+\u003c/u\u003e29.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e44.39\u003cu\u003e+\u003c/u\u003e25.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e42.25\u003cu\u003e+\u003c/u\u003e23.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e41.56\u003cu\u003e+\u003c/u\u003e27.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e44.52\u003cu\u003e+\u003c/u\u003e26.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e41.10\u003cu\u003e+\u003c/u\u003e27.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e39.36\u003cu\u003e+\u003c/u\u003e28.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e43.86\u003cu\u003e+\u003c/u\u003e33.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e39.26\u003cu\u003e+\u003c/u\u003e40.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; SO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e10.9\u003cu\u003e+\u003c/u\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.05\u003cu\u003e+\u003c/u\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.73\u003cu\u003e+\u003c/u\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.39\u003cu\u003e+\u003c/u\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.33\u003cu\u003e+\u003c/u\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.93\u003cu\u003e+\u003c/u\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.40\u003cu\u003e+\u003c/u\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.87\u003cu\u003e+\u003c/u\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003eN/A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; NO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003cu\u003e+\u003c/u\u003e4.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e11.75\u003cu\u003e+\u003c/u\u003e5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e8.93\u003cu\u003e+\u003c/u\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.99\u003cu\u003e+\u003c/u\u003e2.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e8.00\u003cu\u003e+\u003c/u\u003e4.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.59\u003cu\u003e+\u003c/u\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.32\u003cu\u003e+\u003c/u\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.29\u003cu\u003e+\u003c/u\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.69\u003cu\u003e+\u003c/u\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; CO-1hr.(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003cu\u003e+\u003c/u\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003cu\u003e+\u003c/u\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.49\u003cu\u003e+\u003c/u\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.64\u003cu\u003e+\u003c/u\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003cu\u003e+\u003c/u\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.45\u003cu\u003e+\u003c/u\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.42\u003cu\u003e+\u003c/u\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.63\u003cu\u003e+\u003c/u\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.59\u003cu\u003e+\u003c/u\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ozone-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e23.20\u003cu\u003e+\u003c/u\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e25.34\u003cu\u003e+\u003c/u\u003e9.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e25.47\u003cu\u003e+\u003c/u\u003e10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e24.94\u003cu\u003e+\u003c/u\u003e10.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e24.77\u003cu\u003e+\u003c/u\u003e10.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e21.84\u003cu\u003e+\u003c/u\u003e9.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e25.11\u003cu\u003e+\u003c/u\u003e12.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e19.64\u003cu\u003e+\u003c/u\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e18.92\u003cu\u003e+\u003c/u\u003e10.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"100%\" colspan=\"10\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory outcome (duration 2013-2021), Mean \u003cu\u003e+\u003c/u\u003e SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; COPD death case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e6.68\u003cu\u003e+\u003c/u\u003e4.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e10.83\u003cu\u003e+\u003c/u\u003e4.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3.68\u003cu\u003e+\u003c/u\u003e2.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e7.48\u003cu\u003e+\u003c/u\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e4.90\u003cu\u003e+\u003c/u\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e8.24\u003cu\u003e+\u003c/u\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.98\u003cu\u003e+\u003c/u\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12.50\u003cu\u003e+\u003c/u\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.13\u003cu\u003e+\u003c/u\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; COPD mortality rate/1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.37\u003cu\u003e+\u003c/u\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.91\u003cu\u003e+\u003c/u\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.55\u003cu\u003e+\u003c/u\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.14\u003cu\u003e+\u003c/u\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e1.67\u003cu\u003e+\u003c/u\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.90\u003cu\u003e+\u003c/u\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.46\u003cu\u003e+\u003c/u\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.64\u003cu\u003e+\u003c/u\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003cu\u003e+\u003c/u\u003e0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; COPD re-admission case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e80.54\u003cu\u003e+\u003c/u\u003e60.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e189.83\u003cu\u003e+\u003c/u\u003e41.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e36.54\u003cu\u003e+\u003c/u\u003e11.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e114.73\u003cu\u003e+\u003c/u\u003e37.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e45.68\u003cu\u003e+\u003c/u\u003e17.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e53.79\u003cu\u003e+\u003c/u\u003e19.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e45.48\u003cu\u003e+\u003c/u\u003e15.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e131.53\u003cu\u003e+\u003c/u\u003e37.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e27.51\u003cu\u003e+\u003c/u\u003e8.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; COPD re-admission rate/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e16.16\u003cu\u003e+\u003c/u\u003e4.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e16.48\u003cu\u003e+\u003c/u\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e15.21\u003cu\u003e+\u003c/u\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e18.47\u003cu\u003e+\u003c/u\u003e3.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e15.82\u003cu\u003e+\u003c/u\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e12.37\u003cu\u003e+\u003c/u\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e14.05\u003cu\u003e+\u003c/u\u003e3.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e17.78\u003cu\u003e+\u003c/u\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e18.91\u003cu\u003e+\u003c/u\u003e5.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lung cancer death case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e4.61\u003cu\u003e+\u003c/u\u003e3.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e7.86\u003cu\u003e+\u003c/u\u003e2.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e2.26\u003cu\u003e+\u003c/u\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.47\u003cu\u003e+\u003c/u\u003e2.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e3.11\u003cu\u003e+\u003c/u\u003e2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e6.62\u003cu\u003e+\u003c/u\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e3.37\u003cu\u003e+\u003c/u\u003e1.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e7.28\u003cu\u003e+\u003c/u\u003e3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e1.21\u003cu\u003e+\u003c/u\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.75%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Lung cancer mortality rate/1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e6.95\u003cu\u003e+\u003c/u\u003e5.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e2.85\u003cu\u003e+\u003c/u\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e5.61\u003cu\u003e+\u003c/u\u003e3.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e4.31\u003cu\u003e+\u003c/u\u003e1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.333333333333334%\" valign=\"top\"\u003e\n \u003cp\u003e8.87\u003cu\u003e+\u003c/u\u003e5.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e10.58\u003cu\u003e+\u003c/u\u003e4.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e7.38\u003cu\u003e+\u003c/u\u003e3.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e5.53\u003cu\u003e+\u003c/u\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" valign=\"top\"\u003e\n \u003cp\u003e10.72\u003cu\u003e+\u003c/u\u003e9.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eN/A = no data\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eComparison of Air Pollutant Levels and Respiratory Diseases Between Haze and Non\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003ehaze seasons\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVisualization of air pollutants in eight provinces of upper northern Thailand between haze and non-haze seasons are presented in Figure 3.\u0026nbsp;The levels of PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u0026nbsp;\u003c/sub\u003e,PM\u003csub\u003e10\u003c/sub\u003e and O\u003csub\u003e3\u0026nbsp;\u003c/sub\u003eof all provinces during haze season were higher than non-haze season.\u0026nbsp;The highest PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u0026nbsp;\u003c/sub\u003elevels during haze season was found in Mae Hong Son\u0026nbsp;(50.7 ug/m\u003csup\u003e3\u003c/sup\u003e), Chiang Rai\u0026nbsp;(46.3 ug/m\u003csup\u003e3\u003c/sup\u003e), and Chiang Mai\u0026nbsp;(44.0 ug/m\u003csup\u003e3\u003c/sup\u003e), respectively.\u0026nbsp;However, the levels of other pollutants, which included SO\u003csub\u003e2\u003c/sub\u003e, NO\u003csub\u003e2\u003c/sub\u003e, and CO, among eight provinces were rather similar both in haze and non-haze season.\u0026nbsp;When comparing air pollutant levels and respiratory diseases between haze and non-haze seasons by using independent sample t-test, the results found that the levels of all parameters of air pollutants during haze season were significantly higher than those during non-haze season\u0026nbsp;(P value\u0026lt;0.01).\u0026nbsp;(Table2)\u003c/p\u003e\n\u003cp\u003eMortality and re-admission rate of COPD of all provinces during haze season were higher than non-haze season. \u0026nbsp;Regarding respiratory diseases, mortality and re-admission rates of COPD during haze season were significantly higher than those during non-haze season (P value\u0026lt;0.01). However, there was no difference in mortality rate of lung cancer during haze and non-haze season (Table 2). Furthur details of respiratory diseases in eight provinces of upper northern Thailand between the haze and non-haze seasons are presented in Figure 4\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTable 2\u003c/strong\u003e.\u0026nbsp;Comparison of Air Pollutant Levels and Respiratory Diseases between hthe Haze and Non-haze Seasons\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eHaze season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003eNon-haze season\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003eMean difference\u003cu\u003e+\u003c/u\u003eSE. (95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003eP-value\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eAir pollutants\u0026nbsp;(duration 2013-2022)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e-24hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e42.91\u003cu\u003e+\u003c/u\u003e25.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e12.14\u003cu\u003e+\u003c/u\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e30.77\u003cu\u003e+\u003c/u\u003e1.95\u0026nbsp;(28.42, 33.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; PM\u003csub\u003e10\u003c/sub\u003e-24hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e61.44\u003cu\u003e+\u003c/u\u003e30.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e22.59\u003cu\u003e+\u003c/u\u003e7.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e38.85\u003cu\u003e+\u003c/u\u003e1.43\u0026nbsp;(36.05, 41.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; SO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.18\u003cu\u003e+\u003c/u\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.01\u003cu\u003e+\u003c/u\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e0.17\u003cu\u003e+\u003c/u\u003e0.59\u0026nbsp;(0.05, 0.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.004*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; NO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e8.47\u003cu\u003e+\u003c/u\u003e5.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e4.46\u003cu\u003e+\u003c/u\u003e3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e4.00\u003cu\u003e+\u003c/u\u003e0.27\u0026nbsp;(3.47, 4.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; CO-1hr.(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e0.66\u003cu\u003e+\u003c/u\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003cu\u003e+\u003c/u\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003cu\u003e+\u003c/u\u003e0.16\u0026nbsp;(0.18, 0.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Ozone-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e30.53\u003cu\u003e+\u003c/u\u003e10.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e15.88\u003cu\u003e+\u003c/u\u003e5.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e14.65\u003cu\u003e+\u003c/u\u003e0.52\u0026nbsp;(13.64, 15.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"51.02040816326531%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003eRespiratory \u0026nbsp;outcomes (duration 2013-2021)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Mortality rate of COPD /1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e1.59\u003cu\u003e+\u003c/u\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e1.16\u003cu\u003e+\u003c/u\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003cu\u003e+\u003c/u\u003e0.06\u0026nbsp;(0.32, 0.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Re-admission rate of COPD /1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e18.29\u003cu\u003e+\u003c/u\u003e4.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e14.06\u003cu\u003e+\u003c/u\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e4.23\u003cu\u003e+\u003c/u\u003e0.29\u0026nbsp;(3.66, 4.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026lt;0.001*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"36.734693877551024%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Mortality rate of lung cancer /1,000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e7.15\u003cu\u003e+\u003c/u\u003e5.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\" valign=\"top\"\u003e\n \u003cp\u003e6.74\u003cu\u003e+\u003c/u\u003e5.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e0.41\u003cu\u003e+\u003c/u\u003e0.39\u0026nbsp;(-0.36,1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.204081632653061%\" valign=\"top\"\u003e\n \u003cp\u003e0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003eIndependent t-test, * p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 The Association of Air Pollutant Levels with Mortality Rates of COPD and Lung Cancer, and Re\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eadmission rates of COPD\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results found that mortality rate of COPD was weakly positive association with PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e, PM\u003csub\u003e10\u003c/sub\u003e, SO2, NO2, and ozone\u0026nbsp;(P-value \u0026lt;0.01).\u0026nbsp;Re-admission rate of COPD was moderately positive association with PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e , PM\u003csub\u003e10\u003c/sub\u003e, NO2, CO, and ozone\u0026nbsp;(P-value \u0026lt;0.01).\u0026nbsp;Regarding mortality of lung cancer, there were no association with PM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e and PM\u003csub\u003e10\u003c/sub\u003e, but there were weakly negative association with SO2 , NO2, and CO. Additional association was showed in Table 3.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Pearson Correlation Coefficient (\u003cem\u003er\u003c/em\u003e)\u0026nbsp;of Air Pollutant Levels with Mortality Rates of Lung Cancer and COPD, and Re-admission Rates of COPD\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"53.75%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eCOPD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eLung cancer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"34.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003e1,000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eRe\u003c/strong\u003e\u003cstrong\u003e-\u003c/strong\u003e\u003cstrong\u003eadmission rate\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003e1000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.636363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eMortality rate\u003c/strong\u003e\u003cstrong\u003e/\u003c/strong\u003e\u003cstrong\u003e1,000\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003ePM\u003csub\u003e2\u003c/sub\u003e\u003csub\u003e.\u003c/sub\u003e\u003csub\u003e5\u003c/sub\u003e-24 hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.233**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.446**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003ePM\u003csub\u003e10\u003c/sub\u003e-24 hrs., ug/m\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.288**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.495**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eSO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.136**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e-0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.133**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eNO\u003csub\u003e2\u003c/sub\u003e-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.171**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.315**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.149**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eCO-1hr.(ppm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.308**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.098**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.61111111111111%\" valign=\"top\"\u003e\n \u003cp\u003eOzone-1hr.(ppb)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.25%\" valign=\"top\"\u003e\n \u003cp\u003e0.257**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.5%\" valign=\"top\"\u003e\n \u003cp\u003e0.343**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.63888888888889%\" valign=\"top\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e+\u003c/sup\u003ePearson correlation; **P value \u0026lt;0.01\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe air pollution in the northern part of Thailand followed a predictable seasonal pattern, occurring consistently throughout the same period each year (Punsompong \u0026amp; Chantara, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Suriyawong et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The causes of air pollution in this region involve a multitude of aspects, including pollution sources, weather conditions, and atmospheric conditions. The chemical composition of PM2.5 might change depending on meteorological circumstances and emission sources, resulting in varied adverse health effects in different regions (Bran et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). When reviewing the provincial level, it was discovered that the problem is particularly noticeable in Mae Hong Son and Chiang Rai. Both provinces encounter border-related issues, suggesting a cross-border influence (Engling et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Punsompong, Pani, Wang, \u0026amp; Bich Pham, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn June 2023, Thailand established an updated acceptable PM\u003csub\u003e2.5\u003c/sub\u003e standard, which, however, remains higher than the level set by the World Health Organization. The existing WHO guidelines indicate that the average yearly levels of PM\u003csub\u003e2.5\u003c/sub\u003e should not surpass 5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e. Additionally, the average daily exposure should not exceed 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e for more than three to four days per year(World Health Organization (WHO), \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) (World Health Organization (WHO), \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, Thailand's recent announcement stated that the annual average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e should not exceed 15 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, and the 24-hour average exposures should not exceed 37.5 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (Pollution Control Department (PCD), \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It suggests that the previous efforts to manage air pollution in this area were not efficient. Hence, it may be important to implement enhanced measures to deal with the root cause of pollution.\u003c/p\u003e \u003cp\u003eCOPD are known for acute onset exacerbated by air pollutants. The study revealed a correlation between COPD and ambient air pollution levels. Exposure to pollutants were notably associated with increased hospital readmissions and mortality in COPD cases, and the trend of COPD cases aligned with the levels of air pollutants. Research conducted globally consistently indicates that air pollution significantly influences respiratory diseases in both short-term (Gao, Wang, W, Zhao, \u0026amp; Xia, 2020; Han, Pak, Lee, \u0026amp; Chung, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Orellano, Reynoso, Quaranta, Bardach, \u0026amp; Ciapponi, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and long-term exposures(Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Beelen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), leading to increased hospital admissions and mortality rates. Earlier research revealed a link between COPD hospitalizations and air pollution levels, especially concerning PM2.5, PM10, O3, and NO(Andersen et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Han et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Exposure to PM10 and PM2.5 has been positively linked to an increased risk of respiratory mortality, ranging from 1.0073 to 1.023 (Areal, Zhao, Wigmann, Schneider, \u0026amp; Schikowski, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Orellano et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Findings from a multicenter cohort project study on long-term exposure revealed that even exposure to concentrations less than 20 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e led to a rise in mortality rates (Beelen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In line with our research, studies conducted in northern Thailand have revealed associations between PM2.5, PM10, and O3 with COPD(Varapongpisan, Frank, \u0026amp; Ingsrisawang, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), leading to increased acute exacerbations and visits to the emergency room(Pothirat et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Surit, Wongtanasarasin, Boonnag, \u0026amp; Wittayachamnankul, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The increased rates of COPD readmission and mortality may be attributed to the impact of PM on pulmonary function (Bloemsma, Hoek, \u0026amp; Smit, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Edginton, O'Sullivan, King, \u0026amp; Lougheed, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), specifically focusing on forced expiratory volume in one second (FEV1), a primary indicator of COPD mortality. As a result, heightened exposure to PM2.5 which decreases FEV1 values, results in adverse outcomes, contributing to increased hospitalization and mortality.\u003c/p\u003e \u003cp\u003eDuring haze, there was an observed increase in the mortality rate for lung cancer; nevertheless, a statistically significant difference from the non-haze season was not found. Contrary to findings in other Thai studies, previous research indicated that the population-attributable fraction of PM2.5 for lung cancer was 16.8% in the Thai population (Pinichka et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additionally, the reported number of deaths from lung cancer attributed to PM2.5 and PM10 in Northern Thailand was approximately 0.04% and 0.06%, respectively(Supasri, Gheewala, Macatangay, Chakpor, \u0026amp; Sedpho, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This could be clarified by considering that lung cancer has a prolonged period of exposure leading to fatalities. Cumulative doses may accumulate over time before ultimately resulting in death. Based on a study conducted in the U.S.(Pun, Kazemiparkouhi, Manjourides, \u0026amp; Suh, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), the risk of lung cancer mortality increased from 1.13 to 1.33 times when transitioning from a 12-month moving average PM2.5 exposure to a 60-month moving average exposure. This study exclusively analyzed year-by-year mortality rates without considering cumulative effects, latency periods, or lag times.\u003c/p\u003e \u003cp\u003eThis study demonstrated the predictable pattern of seasonal haze, which consistently exhibited an upward trend from January to April annually. These robust findings call for decisive action from stakeholders, including governmental bodies, environmental agencies, public health officials, and healthcare providers. Proactive measures are crucial for ensuring a well-prepared and timely public health response in anticipation of the upcoming haze season. The findings reveal significant health impacts of air pollution on respiratory diseases. Consequently, it becomes imperative for authorities to thoroughly investigate the root causes of the pollution problems. Implementing policies or legal measures to target these root causes can pave the way for a more effective and lasting solution.\u003c/p\u003e \u003cp\u003eThis study has a distinct strength in its focus on northern Thailand, an area globally recognized for its severe air pollution issues. Utilizing a decade-long dataset from a reliable standard source contributes to the robustness of our findings. The visualization of our data is not only comprehensive and easily interpretable, offering valuable insights that can be seamlessly integrated into evidence-based decision-making processes, and public health initiatives. However, this study has a several limitations. Firstly, the study focused on the individual impact of each pollutant on the outcomes, potentially missing synergistic effects on the respiratory system, where multiple pollutants may influence the same organ outcomes. Second, the study used a year-by-year cross-sectional analysis, which may not fully account for latency periods or lag times. Lastly, constrained by the limitations of the health data reporting system, the study lacked comprehensive information such as specific population.s Sub-analyses within specific groups, such as gender and age, were not conducted, potentially preventing the identification of distinct results. In future studies, it is advisable to adopt a comprehensive approach by considering the combined impact of multiple pollutants on outcomes. Utilizing cohort designs, moving average calculation, or advanced time series analysis methods can enhance the understanding of associations. Given the evident health impacts of air pollution and the predictability of seasonal haze, studies on reducing re-admissions and mortality rate are recommended.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe seasonal haze in northern Thailand follows a consistent trend from January to April, presenting a predictable pattern. The study established an association between air pollution, specifically PM2.5, PM10, and O3, with the mortality and re-admission rates of COPD. These findings emphasize the importance of an early and well-prepared public health response, especially before the haze season. Recommending the implementation of policies or legal measures to target the root causes of air pollution emerges as a crucial step toward a more effective and lasting solution.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Research Ethics Committee of the Faculty of Medicine, Chiang Mai University\u0026nbsp;(Study Code : COM-2566-0249).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe air pollution data are available as open data via the Pollution Control Department online data repository: http://air4thai.pcd.go.th, The anonymised data collected are available as open data via the Health Region 1,Ministry of Public Health online data repository (in Thai) : https://cmi.ciorh1.com/web/index.php\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePachara Sapbamrer:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing - original draft, Writing - review \u0026amp; editing.; \u003cstrong\u003ePheerasak Assavanoppkhun:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Methodology, Validation, Writing - original draft, Writing - review \u0026amp; editing.; \u003cstrong\u003eJinjuta Panumasvavat:\u0026nbsp;\u003c/strong\u003eConceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Writing - original draft, Writing - review \u0026amp; editing.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI in scientific writing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used ChatGPT in order to improve readability and language of the work. After using this tool, the authors reviewed and edited the content as needed and takes full responsibility for the content of the publication.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAndersen, Z. J., Bonnelykke, K., Hvidberg, M., Jensen, S. S., Ketzel, M., Loft, S., . . . Raaschou-Nielsen, O. (2012). Long-term exposure to air pollution and asthma hospitalisations in older adults: a cohort study. \u003cem\u003eThorax, 67\u003c/em\u003e(1), 6-11. https://doi.org/10.1136/thoraxjnl-2011-200711\u003c/li\u003e\n\u003cli\u003eAreal, A. T., Zhao, Q., Wigmann, C., Schneider, A., \u0026amp; Schikowski, T. (2022). The effect of air pollution when modified by temperature on respiratory health outcomes: A systematic review and meta-analysis. \u003cem\u003eSci Total Environ, 811\u003c/em\u003e, 152336. https://doi.org/10.1016/j.scitotenv.2021.152336\u003c/li\u003e\n\u003cli\u003eBeelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z. 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Phairuang, W. (2023). Airborne particulate matter from biomass burning in Thailand: Recent issues, challenges, and options. \u003cem\u003eHeliyon, 9\u003c/em\u003e(3), e14261. https://doi.org/10.1016/j.heliyon.2023.e14261\u003c/li\u003e\n\u003cli\u003eVarapongpisan, T., Frank, T. D., \u0026amp; Ingsrisawang, L. (2022). Association between out-patient visits and air pollution in Chiang Mai, Thailand: Lessons from a unique situation involving a large data set showing high seasonal levels of air pollution. \u003cem\u003ePLoS One, 17\u003c/em\u003e(8), e0272995. https://doi.org/10.1371/journal.pone.0272995\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2021). In \u003cem\u003eWHO global air quality guidelines: Particulate matter (PM(2.5) and PM(10)), ozone, nitrogen dioxide, sulfur dioxide and carbon monoxide\u003c/em\u003e. Geneva.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization (WHO). (2022). \u003cem\u003eBillions of people still breathe unhealthy air: new WHO data\u003c/em\u003e. Retrieved from Geneva, Switzerland: https://www.who.int/news/item/04-04-2022-billions-of-people-still-breathe-unhealthy-air-new-who-data\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":"air pollution, particulate matter, respiratory disease, lung cancer, chronic obstructive pulmonary disease","lastPublishedDoi":"10.21203/rs.3.rs-3875948/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3875948/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUpper northern Thailand faced a crisis of air pollution, posing significant challenges to respiratory health. This study aimed to investigate the trends and associations between air pollutant levels and lung cancer and chronic obstructive pulmonary disease (COPD). This study spanned eight provinces over 2013\u0026ndash;2022, collecting air pollutant monitoring data from the Pollution Control Department and respiratory health information, including mortality rates for lung cancer and COPD patients, along with the readmission rate for COPD patients, from Regional Public Health. The dataset was divided into two seasons, namely, the haze (December-May) and non-haze (June-November) seasons. The findings indicated a decadal pattern, with peak levels observed in March for all air pollutant parameters and COPD readmission rates. The PM2.5 concentration exceeded Thailand's air quality standards from January to April. COPD mortality and readmission rates significantly increased compared to those in the non-haze periods (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). While lung cancer mortality rates were greater in the haze season, the difference was not statistically significant. Pearson correlation analysis indicated moderate positive associations between PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, CO, and NO\u003csub\u003e2\u003c/sub\u003e levels and COPD readmission rates (r\u0026thinsp;=\u0026thinsp;0.308 to 0.495, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Moreover, the PM\u003csub\u003e10\u003c/sub\u003e, PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e concentrations exhibited a weak positive association with the COPD mortality rate (r 0.014 to 0.288, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Upper northern Thailand experienced a predictable pattern of air pollution, positively linked to higher COPD death and readmission rates. These findings highlight the need for an early and well-prepared public health response, especially before the haze season.\u003c/p\u003e","manuscriptTitle":"The Ecological Study on Decadal Trends and Impacts of Ambient Air Pollutants on COPD and Lung Cancer in Upper Northern Thailand: 2013-2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-21 18:17:25","doi":"10.21203/rs.3.rs-3875948/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":"c521f6ed-53e8-4de8-80e2-12e76290536f","owner":[],"postedDate":"February 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-23T13:14:49+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-21 18:17:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3875948","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3875948","identity":"rs-3875948","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}