Assessment of the Health and Economic Benefits of PM 2.5 -O 3 Composite Pollution in Henan Province, China, 2020–2024

Assessment of the Health and Economic Benefits of PM 2.5 -O 3 Composite Pollution in Henan Province, China, 2020–2024 | 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 Article Assessment of the Health and Economic Benefits of PM 2.5 -O 3 Composite Pollution in Henan Province, China, 2020–2024 Liangkui Wei, Yalun Cheng, Dan Wang, Zhengke Si, Ruotong Liu, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8807618/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract This study analyzes the spatiotemporal evolution of PM 2.5 and O 3 pollution across 17 cities in Henan Province, China, from 2020 to 2024, assessing associated health and economic impacts. Results indicate that while PM 2.5 levels showed a general decline, O 3 concentrations exhibited a fluctuating upward trend, with more pronounced pollution in northern and central regions. March–May and September–October were identified as peak periods for composite pollution. In 2024, PM 2.5 -related premature deaths decreased by 10.6–12.0% compared to 2020, while O 3 -related deaths increased by approximately 22.0%. Scenario projections suggest that achieving Class I standards of GB 3095 − 2012 by 2030 could reduce premature deaths attributable to PM 2.5 and O 3 by 83.3% and 13.5%, respectively, with potential economic benefits of RMB 65.8 billion. The findings emphasize the need for coordinated pollution control to maximize health and economic benefits. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Social science/Environmental studies fine particulate matter ozone composite pollution health benefits economic benefits Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction As China’s urbanization process continues to advance, the composition of air pollutants is shifting from a single soot type to a combination of various pollutants, resulting in composite pollution [ 1 ]. This transition is particularly evident in the cases of PM 2.5 and O 3 composite pollutants, which have become increasingly prominent. With the promulgation and implementation of the Action Plan for Air Pollution Prevention and Control and other measures, PM 2.5 concentrations in China’s cities have shown a marked decline. However, the concentration is still much higher than the targets set out in the World Health Organization’s ‘Global Air Quality Guidelines’ [ 2 , 3 ]. At the same time, O 3 pollution has become increasingly severe, especially in major urban agglomerations such as the Central Plains, Beijing-Tianjin-Hebei, and the Yangtze River Delta, where O 3 concentrations are 25% to 40% higher than in other regions [ 4 , 5 ]. Human exposure to polluted air, especially heavily polluted air, has been demonstrated to have a detrimental effect on the cardiovascular, respiratory, and immune systems [ 6 ]. Air pollution has become one of the major health risk factors worldwide. For instance, the Global Air Quality Report 2024 asserts that air pollution was the fourth most significant global mortality risk factor in 2019, resulting in approximately 6.7 million deaths due to long-term exposure-related risks. Furthermore, it is reported that more than 90% of the global population still resides in areas with high levels of air pollution. This has led to scholars in various countries paying continuous attention to the health effects of air pollution. Numerous studies have confirmed that atmospheric PM 2.5 is a key health killer, and long-term exposure significantly increases the risk of chronic obstructive pulmonary disease (COPD), stroke, lung cancer, lower respiratory tract infections, and ischemic heart disease. The risk of cardiovascular deaths increases by 6–8% for every 10 µg/m 3 increase in concentration [ 7 ]; at the same time, as a strong oxidant, O 3 , once inhaled, will rapidly attack the respiratory epithelial cells, leading to cell membrane rupture and cell death, and disrupting lung barrier function [ 8 ]. Ozone also irritates the eyes and respiratory tract, and has an effect on the central nervous system, leading to impaired vigilance and performance [ 9 ]. Based on the analysis of time-series data from 372 cities worldwide, we found that PM 2.5 and O 3 had synergistic additive effects on all-cause, cardiovascular, and respiratory deaths (synergistic indices 1.4–2), and the number of premature deaths caused by their combined exposure was much greater than the sum of separate exposures [ 10 ]. Henan Province, a substantial agricultural region and a major demographic hub in China, provides a critical context for studying composite pollution. With a population of 98.2 million at the end of 2024, ranking third in the country, its high population density increases the public health burden of air pollution. The agricultural activities, particularly biomass burning and ammonia emissions from fertilizer use, are significant precursors for secondary PM 2.5 formation [ 11 , 12 ]. Concurrently, the province’s intensive industrial and urban development exacerbates local emissions. In Henan, the primary sources of PM 2.5 include industrial emissions (e.g., steel, coking, and cement production), coal combustion, vehicle exhaust, and agricultural residue burning [ 11 , 12 ]. O 3 is primarily generated through photochemical reactions involving NO x and VOCs, emitted largely from transportation, industrial processes, and solvent use [ 4 , 13 ]. The northern and central regions, such as Zhengzhou and Anyang, exhibit higher emissions due to concentrated industrial and traffic activities, consistent with their higher observed pollutant concentrations [ 12 – 14 ]. Given these diverse and intense emission sources, it is unsurprising that Henan Province ranks low among 168 key cities in the country for air quality. Eight cities (Xinxiang, Anyang, Hebi, Jiaozuo, Pingdingshan, Luoyang, Zhengzhou, and Xuchang) make up the bottom twenty in 2024, accounting for the largest proportion of cities. Xinxiang ranks in the bottom three in the country [ 14 , 15 ], and is listed as a priority for PM 2.5 -O 3 synergistic management by the State Council. Based on the 2020–2024 national ambient air quality monitoring station data from 17 cities in Henan Province, this study used the environmental health effect assessment method to investigate the spatial and temporal distribution characteristics of PM 2.5 and O 3 pollution and the health impacts and economic losses caused by pollution exposure in Henan Province, with a view to providing references for the development of air pollution prevention and health protection measures in Henan Province. Despite numerous studies on air pollution in China, few have simultaneously assessed the health and economic impacts of both PM 2.5 and O 3 in a highly industrialized and agriculturally active region like Henan. Moreover, existing studies often neglect the compounding effects of these pollutants and their interactive health burdens [ 10 ]. This study fills this gap by integrating high-resolution monitoring data, advanced health impact assessment modeling, and scenario analysis to provide actionable insights for regional air quality management, building on the foundational approaches of prior burden-of-disease studies [ 16 , 17 ]. 2. Materials and Methods 2.1. Data Sources and Processing Hourly PM 2.5 and O 3 concentration data (1 January 2020–31 December 2024) for 17 cities in Henan Province (except Ji yuan) were obtained from the National Urban Air Quality Real-Time Dissemination Platform of the China Environmental Monitoring General Station ( https://air.cnemc.cn:18007 , accessed on 30 November 2025). The population data of all cities in Henan Province was obtained from the statistical yearbooks of each city. Per capita disposable income data was obtained from the Henan Provincial Bureau of Statistics. Baseline mortality rates for different health effect endpoints, such as all-cause premature deaths (A00-Y98), premature deaths of the cardiovascular system (I00-I99), and premature deaths of the respiratory system (J00-J99), were obtained from the China Health and Wellness Statistical Yearbook (Volume 2023). We applied the China National Environmental Monitoring Centre’s standard procedures to remove outliers and ensure consistency across stations [ 18 ]. Data completeness was ensured by requiring at least 75% valid hourly records per day for inclusion in daily averages [ 19 ]. The daily average O 3 concentration was calculated using the maximum 8 h moving average (O 3 _8h_max), and the 90% percentile of the daily O 3 _8h_max concentration was used for the annual evaluation value of O 3 concentration. According to the Chinese National Ambient Air Quality Standard (GB 3095 − 2012) [ 18 ], a pollution day was defined as having daily PM 2.5 concentrations exceeding 35 µg/m 3 or daily maximum 8 h average O 3 concentrations exceeding 100 µg/m 3 ; concurrent exceedances of both thresholds were classified as compound pollution [ 1 , 20 ]. The adoption of the 100 µg/m 3 O 3 threshold aligns with China’s regulatory framework and ensures direct policy relevance to Henan Province, although it differs from the stricter WHO guideline of 100 µg/m 3 (daily maximum 8 h average) [ 3 ] and recommendations in other international studies [ 4 ]. This approach follows the established methodology for assessing composite pollution in China’s regulatory context [ 20 , 21 ]. 2.2. Consideration of the COVID-19 Impact on 2020 Data It is important to note that the year 2020 was significantly affected by the COVID-19 pandemic, which led to widespread lockdowns and a substantial reduction in economic and social activities. This resulted in anomalously low levels of air pollution, particularly during the first half of the year [ 22 ]. While the data for 2020 are included in our study to provide a complete temporal picture, its unique nature is acknowledged. In the trend analysis, 2020 is treated as a unique baseline year, and discussions of interannual trends primarily focus on the period from 2021 to 2024 to avoid potential bias from this anomalous period. 2.3. Population Health Effect Assessment Methods The number of premature deaths attributable to PM 2.5 and O 3 pollution in cities in Henan Province from 2020 to 2024 was estimated using Benmap-CE 1.5. This was based on PM 2.5 and O 3 exposure data, baseline mortality, population data, and exposure–response relationship coefficients [see Eq. ( 1 )] [ 23 ]. The health effect endpoints selected for this study included all-cause premature death, premature death due to respiratory system diseases, and premature death due to cardiovascular system diseases [ 16 ]. $$\:{Y}_{i}={Y}_{0}\left(1-{e}^{-\beta\:\times\:{X}_{i}}\right)$$ 1 The baseline concentration of PM 2.5 adopts GB 3095 − 2012 ‘Ambient Air Quality Standards’ Class I standard limit value (15 µg/m 3 , O 3 baseline concentration takes the value of 70 µg/m 3 ) [ 24 ]; \(\:\:\beta\:\) is the exposure–response relationship coefficient (see Table 1 ), which indicates the percentage increase in the risk of death for different health effect endpoints for each 10 µg/m 3 increase in PM 2.5 and O 3 concentrations; e is a natural constant The exposure–response coefficients in Table 1 are validated for health impact assessments in China [ 17 , 25 , 26 ], having been derived from large-scale epidemiological studies in China and comparable regions [ 25 , 26 ]. Their established application in central China, which accounts for demographic factors, supports their use for the Henan context [ 17 ]. The total economic loss attributable to air pollution was obtained by summing the separately calculated losses for PM 2.5 and O 3 . This approach aligns with the health impact assessment framework and assumes no economic interaction between the losses caused by the two pollutants. Table 1 Exposure response relationship coefficients of different health outcomes. Pollutant Health Effects Endpoint \(\:\varvec{\beta\:}\) (Exposure–Response Relationship Coefficient) PM 2.5 All because of an early death. 0.021 76 (0.013 90, 0.029 56) Premature death from diseases of the cardiovascular system 0.021 76 (0.009 95, 0.034 40) Premature death from respiratory diseases 0.088 93 (0.058 27, 0.119 56) O 3 All because of an early death. 0.008 10 (0.005 08, 0.011 10) Premature death of the cardiovascular system 0.011 10 (0.002 04, 0.020 50) Premature death from respiratory diseases 0.014 59 (0.005 58, 0.023 43) 2.4. Methods for Assessing Economic Losses from Health Effects The monetary value of a statistical life (VSL) is utilized to quantify the financial losses incurred as a result of premature mortality. The VSL is a metric of the marginal willingness to pay for the avoidance of the risk of death [ 27 ]. In accordance with the findings of preceding studies [ 28 ], the baseline was set at RMB 168 × 104. The VSL was adjusted by correcting the per capita disposable income of 17 cities in Henan Province from 2020 to 2024, and the formula was calculated as follows: $$\:VS{L}_{j,n}={VSL}_{base}\times\:({\frac{{I}_{j,n}}{{I}_{base}})}^{m}$$ 2 where \(\:VS{L}_{j,n}\) is the statistical life value of city j in the nth year, RMB; \(\:{VSL}_{base}\) is the benchmark life value RMB; \(\:{I}_{j,n}\) is the per capita disposable income of city j in Henan province in the nth year, RMB; \(\:{I}_{base}\) is the corresponding per capita disposable income of the benchmark VSL, RMB; m is the coefficient of income elasticity, which is taken as the value of 1. The total economic loss attributable to air pollution was obtained by summing the separately calculated losses for PM 2.5 and O 3 . This approach aligns with the health impact assessment framework and assumes no economic interaction between the losses caused by the two pollutants. This approach assumes independent effects (i.e., additivity). While some epidemiological studies, such as the analysis by Liu et al. (2023) [ 10 ], suggest potential synergistic or antagonistic interactions between PM 2.5 and O 3 , quantitative exposure–response relationships for their combined effects are not yet well-established for long-term mortality endpoints. Therefore, the independent-effect additive model provides a conservative and commonly used baseline estimate, consistent with approaches in studies like Anenberg et al. (2010) [ 16 ]. The implications of this assumption are further discussed in the Uncertainty Analysis section. 2.5. Calculation of Health Risks and Economic Losses in the Target Year Scenario A future target year (2030) scenario was established in order to estimate the number of premature deaths caused by air pollution in the coming year. This estimation was made according to the methodology outlined in Section 2.3 . In addition, the economic losses that can be avoided by achieving the target concentrations in the future were also considered. The promulgation and implementation of national policies for the prevention and control of pollution is expected to result in a further reduction in the concentrations of PM 2.5 and O 3 in the future. In order to assess the number of premature deaths that can be avoided by decreasing the concentrations, as well as the economic benefits that would ensue from such decreases, the target scenarios for PM 2.5 and O 3 concentrations in 2030 are set to have two values. Furthermore, the PM 2.5 and O 3 concentrations in future years are to be reduced to the target concentrations. The resulting health and economic benefits can then be calculated based on the difference between the target scenarios and the baseline scenarios (see Table 2 ). Table 2 Target emission reduction scenario forecast for Henan Province in 2030. Target Scenario Concentration PM 2.5 O 3 1T-1 \(\:35\:{\mu\:}\text{g}/{\text{m}}^{3}\) (GB 3095 − 2012 secondary standard limit) 100 \(\:{\mu\:}\text{g}/{\text{m}}^{3}\) (GB 3095 − 2012 Class I standard limit) 1T-2 \(\:15\:{\mu\:}\text{g}/{\text{m}}^{3}\) 80 \(\:{\mu\:}\text{g}/{\text{m}}^{3}\) Note: If the PM 2.5 and O 3 concentrations in the study cities do not exceed the target scenario concentrations in 2024, the values for that year are used as the target concentrations. 2.6. Consideration of Meteorological Influences We acknowledge that meteorological conditions can significantly influence pollutant concentrations. Due to the lack of a complete and consistent daily meteorological dataset across all 17 cities, we did not perform meteorological adjustment on the reported concentrations. To ensure a robust interpretation of the trends, we focused the core analysis on the post-pandemic period (2021–2024) to minimize the impact of anomalous conditions in 2020. Furthermore, the opposing trends of declining PM 2.5 and rising O 3 observed in this study are consistent with findings from other studies in the North China Plain that have employed meteorological normalization [ 4 , 5 , 29 ]. This consistency strengthens the inference that the dominant trends are primarily driven by emission changes rather than meteorological variability. 3. Results and Discussion 3.1. Characteristics of Spatial and Temporal Variations of PM 2.5 and O 3 Figure 1 illustrates the changes in average concentrations of PM 2.5 and O 3 across 17 cities in Henan Province during different time scales from 2020 to 2024. The annual average PM 2.5 concentration in 2020 established an anomalously low baseline (52 µg/m 3 ) due to the COVID-19 lockdowns [ 22 , 30 ]. Following the resumption of economic activities in 2021, PM 2.5 levels subsequently exhibited a general declining trend through 2024 (Fig. 1 a). In contrast, O 3 concentrations demonstrated a clear and fluctuating upward trajectory over the 2021–2024 period (Fig. 1 b), solidifying its role as the primary pollutant. This divergent trend is aligned with findings from other meteorologically adjusted studies in this region [ 4 , 5 ], suggesting that emission patterns and chemical feedbacks, rather than meteorology, are the dominant drivers. As shown in Fig. 1 a,b, PM 2.5 and O 3 exhibited opposing trends from 2020 to 2024. Contrasted with the GB 3095 − 2012 secondary standard, the proportion of exceedance days for PM 2.5 (75 \(\:{\mu\:}\text{g}/{\text{m}}^{3}\) ) decreased from 19.0% to 16.19%, while that for O 3 (160 \(\:{\mu\:}\text{g}/{\text{m}}^{3}\) ) increased from 11.80% to 15.27%. Since 2022, the number of days on which O 3 has exceeded the standard has been consistently higher than the number of days on which PM 2.5 has done so. This has resulted in O 3 becoming the primary pollution factor. From Fig. 1 c, it can be seen that the PM 2.5 monthly average concentration of the lowest value occurred in July–August, showing a typical ‘U’ distribution, which is mainly attributed to the enhanced turbulent mixing in summer to promote pollutant dilution and diffusion, so that the PM 2.5 concentration is reduced [ 31 ]. In addition, Henan’s winter PM 2.5 concentration is significantly higher than that in other seasons, which is closely related to its emission level, the inversion layer, and static weather [ 32 ]. As shown in Fig. 1 d, the monthly concentration of O 3 exhibits a bimodal seasonal pattern with the primary peak occurring in May-June. Following a slight dip in July, concentrations rise again to form a secondary peak in August-September. However, statistical analysis ( p > 0.05) indicates that the differences in O 3 concentrations among July, August, and September are not significant. Therefore, the observed dip in July does not constitute a statistically robust trough, and the pattern is more accurately described as a prolonged period of elevated O 3 levels from late spring through early autumn, with the highest concentrations in late spring (May-June). The main reason is that the two periods of May-June and August-September satisfy the high temperature, strong light, and low humidity conditions required for O 3 generation, and the concentration falls back in July due to the increase in cloudiness and precipitation frequency, and the weakening of solar radiation. Furthermore, for every 1 µg m − 3 reduction in Henan’s annual average PM 2.5 concentration between 2020 and 2024, indirect increases in O 3 levels of approximately 0.3–0.4 µg m − 3 may occur. This is achieved by reducing aerosol surface light absorption and enhancing HO 2 radical production, consistent with existing chemical transport simulation results for the North China Plain [ 4 , 5 ]. Concurrently, the VOC/NO x ratio in northern cities (Anyang, Hebi) has fallen below 4, placing them within NO x control zones, as observed in regional studies [ 13 ]. Continued unilateral NO x reduction could trigger an O 3 rebound. In summary, the O 3 increase resulted from the combined effects of ‘changes in precursor emissions + enhanced photolysis due to PM 2.5 decline + high-temperature drought conditions’. Future efforts should focus on coordinated control of VOCs and NO x [ 1 , 20 ] while paying attention to the catalytic role of extreme weather events. Figure 2 shows the changes in the average PM 2.5 and O 3 concentrations in Henan Province at different time scales from 2020 to 2024, showing that from 2021 to 2024, the PM 2.5 concentration ranges from 40 to 55 µg/m 3 , presenting a band distribution of ‘high in the north and low in the south’. The area with a concentration of > 50 µg/m 3 has been observed to decrease in size on an annual basis, with only evident residuals remaining in the northern region of Henan and the central portion of Henan Province. This spatial pattern is firstly closely related to regional transport, as the prevailing north wind in winter transports PM 2.5 polluted air masses to Henan Province, resulting in higher PM 2.5 concentrations in the northern part of Henan Province than in the southern part [ 11 ]. However, a single regional transport system is not sufficient to fully explain the persistence and spatial agglomeration of pollution, and local emissions provide an important basis for pollution formation. North and central Henan Province are the traditional industrial and energy core areas, with a concentration of heavy industries such as iron and steel, coking, and building material production, and their high-intensity local emissions, in addition to external transmission, significantly increase the pollution load. Ozone concentrations in areas of high concentration ranged from 100 to 115 µg/m 3 , extending from northern and central Henan to the east. The O 3 pollution observed in these regions is attributable to external factors, such as the transportation of pollutants from North China and the transportation of O 3 by summer southerly winds within the province. Additionally, the emission of substantial quantities of VOCs and NO x by traffic in cities such as Heavy Industry in northeastern Henan and Zhengzhou in central Henan serve as sufficient precursors for O 3 generation [ 13 ]. This spatial disparity is strongly supported by emission inventory and source apportionment studies. The observed pattern aligns with regional emission inventories which identify northern and central Henan as hotspots for industrial production (e.g., steel, coking), energy consumption, and high-density vehicular traffic [ 11 , 13 ]. Model-based source apportionment studies further confirm that regional transport from the North China Plain contributes significantly to the PM 2.5 and O 3 levels in these regions [ 13 , 33 ]. The lower pollution levels in southern and western Henan are thus attributable to a combination of lower local industrial emissions and reduced influence from inter-regional transport. 3.2. PM 2.5 and O 3 Composite Pollution Characteristics As demonstrated in Fig. 3 , the figure plots the number of days of compound pollution and monthly change statistics in Henan Province from 2020 to 2024. The data on the number of days of composite pollution in Henan Province from 2020 to 2024 demonstrates a downward trend, followed by an upward shift. In 2020, the number of days fell to 83. However, due to the suppression of economic and social activities in 2021 as a result of the epidemic, this figure fell to a minimum of 64 days [ 34 ], subsequent to this, there was a marked recovery of the economy and society as a whole, which resulted in a significant increase in the number of days between 2022 and 2024, reaching a peak of 96 days. The primary cause of this rebound trend is the growing issue of O 3 pollution, in the context of PM 2.5 concentrations that have not yet been adequately addressed, resulting in an extended period of overlapping compound pollution. The monthly distribution displays a ‘double-peak’ pattern, with March–April and September–October being the periods with the highest incidence. As demonstrated in Fig. 3 , composite pollution in Henan Province exhibits a pattern of ‘higher levels in the north and lower in the south, higher in the east and lower in the west’; cities in the central-northern region—Anyang, Hebi, and Luohe—consistently experience high pollution levels throughout the year. In contrast, the frequency and intensity of composite pollution episodes in Nanyang in the south and Sanmenxia in the west are significantly lower than those in the central-northern cities. The distinct regional differences are clearly discernible in the spatial distribution. 3.3. Disease Burden Assessment of PM 2.5 and O 3 Pollution Figure 4 illustrates the alteration in the number of premature fatalities attributable to PM 2.5 and O 3 contamination in 17 cities within Henan Province from 2020 to 2024. As demonstrated in Fig. 4 a, the annual average PM 2.5 concentration in 17 cities in Henan Province exhibited a marginal decline during the study period. It is critical to contextualize these reductions relative to the 2020 baseline. The significant decline is partly attributable to the rebound of economic activities and population exposure to pre-pandemic levels [ 35 ], superimposed on the long-term effectiveness of pollution control measures [ 36 ]. Therefore, the trend from 2021 to 2024 provides a more reliable assessment of the genuine improvement in air pollution-related health impacts. The number of premature deaths from all causes, premature deaths from respiratory diseases, and premature deaths from cardiovascular diseases attributable to PM 2.5 pollution in the province in 2024 was divided into 48,169, 14,750, and 22,807 deaths, which were reduced by 12.0%, 10.6%, and 12.0%, respectively, compared with those in 2020. This result shows that Henan has taken many measures to reduce PM 2.5 and significantly cut down the health risk, and the effectiveness of pollution control is obvious. Figure 5 indicates that the overall number of premature deaths attributable to PM 2.5 across 17 cities declined between 2020 and 2024. Zhengzhou recorded the highest absolute number of premature deaths due to its greatest population density, followed by Nanyang and Zhoukou. Conversely, although Anyang and Puyang have smaller populations, high pollution concentrations resulted in a relatively high burden. Consequently, future efforts should focus on high-density population areas to strengthen emission reductions and exposure controls, thereby further mitigating health impacts. Figure 4 b shows that O 3 concentrations in 17 cities in Henan Province showed a decreasing and then increasing trend during the study period, and the number of premature deaths attributable to all-causes, respiratory diseases and cardiovascular diseases in 2024 were 20,577, 3090, and 13,269, respectively, which were increased by 22.0%, 21.8% and 21.9% compared with those in 2020. As demonstrated in Fig. 5 , the cities with the highest number of premature deaths attributable to O 3 pollution are Zhengzhou, Anyang, and Zhoukou. In 2024, the cities demonstrating the most substantial escalation in premature fatalities attributable to O 3 contamination in comparison to 2023 are Shangqiu, Xinxiang, and Luoyang. It is imperative that the endeavors to prevent and control O 3 pollution in these regions remain steadfast. In recent years, with the implementation of a series of low-carbon and emission reduction measures, PM 2.5 pollution levels in different regions have decreased significantly [ 21 ], so the number of premature deaths attributable to PM 2.5 has decreased, but the number of premature deaths attributable to O 3 has shown a rising trend [ 37 ]. From the comparison of the disease burden of premature deaths between the two, the number of premature deaths attributable to PM 2.5 in 17 cities in Henan Province in 2024 was about 2.5 times that of O 3 . 3.4. Assessment of Economic Losses of PM 2.5 and O 3 Pollution The economic losses of all-cause, respiratory diseases and early deaths from cardiovascular diseases attributable to PM 2.5 pollution in Henan Province in 2020–2024 show a decreasing and then stable trend, and the economic losses of the above health effects in 2024 are RMB 89.8 billion, RMB 27.5 billion, and RMB 42.5 billion, respectively, which are compared to 2020 and have, respectively, decreased by 11.5%, 13.3%, and 11.5%. From Fig. 6 the economic losses attributed to different health effects of PM 2.5 pollution in 17 cities in Henan Province showed an overall decreasing trend, and the cities with higher economic losses during the study period were Zhengzhou City, Nanyang City, and Luoyang City, which is related to the higher population density and economic development level of the cities [ 33 , 38 ]. The economic losses attributed to different health effects of O 3 pollution in 17 cities of Henan Province in 2020–2024 showed a decreasing and then increasing trend, and the economic losses of all-cause, respiratory diseases and cardiovascular diseases were RMB 38.8 billion, RMB 5.8 billion, and RMB 25.0 billion in 2024, respectively, which were 55.4%, 55.0% and 55.2%, respectively, higher in comparison with 2020. From Fig. 6 , the economic losses attributed to different health effects of O 3 pollution generally show a fluctuating upward trend, similar to PM 2.5 pollution, and the city with the largest economic loss attributed to O 3 pollution is Zhengzhou City, followed by Anyang City and Xinxiang City. The above study illustrates that the health and economic losses caused by PM 2.5 pollution to 17 cities in Henan Province have gradually stabilized in recent years, while the health and economic losses caused by O 3 pollution to 17 cities in Henan Province have shown a gradual increase in recent years. The number of premature deaths and health and economic losses caused by O 3 pollution have further intensified in a heavily polluted environment, and, in particular, the significant increase in health risks and economic losses caused by cardiovascular system diseases deserves further attention. Therefore, strengthening the synergistic management of PM 2.5 and O 3 pollution to improve the health of the population is the focus of future environmental health efforts. 3.5. Scenario Projections In recent years, the government has taken a number of effective management measures to reduce the concentration of PM 2.5 pollutants, and according to the relevant plan, China’s PM 2.5 concentration is expected to reach the IT-1 standard (35 µg/m 3 ) by 2030 [ 39 ]. Table 3 presents the health benefits and health economic benefits under different scenarios in 2030. By then, if PM 2.5 concentrations in cities in Henan Province were reduced to this standard, the number of premature deaths attributable to PM 2.5 pollution would be reduced by 8896, or 18.5%; however, the health benefits of lower PM 2.5 concentrations are more limited due to the increasing trend of population aging and the implementation of fertility stimulation measures. Some scholars estimates for eastern and central China show that while PM 2.5 -attributable deaths declined by 14.4% from 2013 to 2017, population aging led to a widening of the elderly population base, which offset some of the health benefits-specifically, population aging contributed − 12.4% to the health burden (that is, the exacerbating the burden), while the contribution of PM 2.5 concentration reduction was + 14.6% [ 17 ]. Therefore, further reductions in PM 2.5 target concentrations are needed. In the event of Henan Province’s PM 2.5 concentration attaining the IT-2 scenario (GB 3095 − 2012 Class I standard limit), the number of premature deaths attributable to PM 2.5 pollution would be reduced by 40,143 individuals, representing an 83.3% decrease. The health benefits would be exceptionally significant. Table 3 Health benefits and health economic benefits under different scenarios in 2030. Project Baseline Scenario IT-1 IT-2 Number of premature deaths attributed to PM 2.5 /person 48,169 (31,216, 64,512) 39,273 (25,333, 52,838) 8026 (5137, 10,882) Health economic losses attributed to PM 2.5 /(10 8 RMB) 897.6 (581.7, 1202.1) 953.2 (614.8, 1282.4) 194.8 (124.7, 264.1) Number of avoidable premature deaths attributed to PM 2.5 /person 0 (0, 0) 8896 (5883, 11,674) 40,143 (26,079, 53,630) Avoidable health economic losses attributed to PM 2.5 /(10 8 RMB) 0 (0, 0) −55.6 (− 33.1, − 80.3) 702.8 (457, 938) Number of premature deaths attributed to O 3 /person 20,577 (12,986, 28,025) 17,807 (11,218, 24,293) 5984 (3758, 8188) Health economic losses attributed to O 3 /(10 8 RMB) 387.8 (224.7, 528.2) 432.2 (272.3, 589.6) 145.2 (91.2, 198.7) Number of avoidable premature deaths attributed to O 3 /person 0 (0, 0) 2770 (1768, 3732) 14,593 (9228, 19,837) Avoidable health economic losses attributed to O 3 /(10 8 RMB) 0 (0, 0) −47.6 (− 44.4, − 61.4) 245.5 (133.5, 329.5) According to the analysis of O 3 pollution trends from 2020 to 2024, O 3 pollution shows a fluctuating upward trend, and if the O 3 pollution concentration in cities in Henan Province is reduced to 100 µg/m 3 in 2030, the number of premature deaths caused by it will be significantly reduced by 2770 (95% CI, 1768 to 3732), or about 13.5% (95% CI, 13.3% to 13.6%), compared with the baseline scenario. However, the health benefits of reduced O 3 concentrations are more limited. Therefore, it is necessary to set a higher emission reduction target, such as reducing the O 3 concentration to 80 µg/m 3 , which will reduce the number of premature deaths by 14,593 (95% CI, 9228 to 19837) in 2030. In recent years, there has been a downward trend in atmospheric PM 2.5 exposure concentrations in Henan Province, while O 3 concentrations have exhibited fluctuating increases. It is posited that, based on the 2024 baseline scenario, assuming pollutant concentrations undergo a certain degree of change, reductions in exposure levels for both pollutants can yield divergent health economic benefits. Specifically, when PM 2.5 concentrations meet the IT-1 standard, the improvements in quality of life and increases in per capita disposable income result in an additional RMB 5.56 (95% CI, 3.31 to 8.03) billion in health-related economic losses compared to 2024 pollution levels. Consequently, the implementation of augmented emission reduction targets—such as the reduction of PM 2.5 concentrations to the IT-2 standard—will generate approximately RMB 70.3 (95% CI, 457 to 938) billion in health-related economic benefits. Similarly to PM 2.5 pollution, as per capita disposable income increases, when O 3 concentrations reach 100 µg/m 3 (GB 3095 − 2012 Class I standard limit) by 2030, economic losses relative to the baseline scenario will rise by RMB 4.8 (95% CI, 4.4 to 6.1) billion. Should O 3 concentrations be reduced to the IT-2 standard, losses would decrease by approximately RMB 24.6 (95% CI, 13.3 to 33.0) billion. Overall, pollution reduction yields both health and economic benefits. As pollutant concentrations continue to decline, the marginal health benefits derived from such reductions become increasingly pronounced [ 40 ]. Therefore, on the basis of achieving the existing standards, it is imperative to implement higher standards of emission reduction measures, so as to reduce the health burden and economic losses caused by pollution, and further offset the negative impacts such as population aging. 3.6. Comparative Analysis and Policy Implications The distinct pollution transition observed in Henan—marked by declining PM 2.5 but rising O 3 —reflects a broader national pattern documented in key regions such as Beijing–Tianjin–Hebei and the Yangtze River Delta [ 21 , 37 ]. This divergence underscores that conventional emission control measures, while effective against particulate matter, are inadequate for addressing the non-linear photochemistry driving ozone formation [ 1 , 5 ]. Consequently, a shift toward multi-pollutant synergistic control is urgently needed. Integrated strategies targeting common precursors—especially NO x and VOCs—deliver the greatest co-benefits [ 5 , 13 ]. Our scenario analysis confirms that achieving the Class I standard (IT-2) could prevent approximately 54,700 premature deaths and yield economic benefits of nearly RMB 95 billion, highlighting the significant returns from coordinated action. We thus recommend a focused policy package: accelerating the transition from coal to clean energy in northern and central Henan [ 39 ]; implementing precise VOC and NO x controls in transportation, industrial, and solvent-related sectors [ 1 , 13 ]; and strengthening regional joint prevention mechanisms with neighboring provinces to mitigate cross-boundary pollution [ 13 ]. Such a synergistic pathway is essential to realizing the “Beautiful China” vision and maximizing long-term health and economic co-benefits [ 1 , 5 , 17 ]. 3.7. Uncertainty Analysis The study analyzed the spatial and temporal variations, health effects, and potential health benefits of PM2.5 and O3 concentrations in 17 cities of Henan Province in the target years 2020–2024, with data derived from ground-based environmental monitoring sites. Inherent limitations of such monitoring networks, including finite spatial density (potentially missing gradients within cities) and the representativeness of site locations (e.g., proximity to roads or green spaces), resulted in discrepancies between the population exposure simulation based on station data and the actual population exposure levels. A key limitation of this study is the absence of meteorological adjustment for pollutant concentrations. The observed trends may therefore be confounded by interannual meteorological variability. The limitations of the monitoring network resulted in discrepancies between the population simulation and the actual exposure levels, and further optimization of the exposure assessment is needed in the future. In the selection of health effect endpoints, only all-cause premature death, premature death of cardiovascular system diseases and premature death of respiratory system diseases, which have more research bases at present, were selected, and the possible effects of PM 2.5 and O 3 on other acute and chronic diseases were neglected, which underestimated the impact of the effect results to a certain extent [ 41 , 42 ]. The willingness-to-pay method was used as a measure in assessing the economic loss of health, ignoring the true cost of health services, and the relationship between air pollution and the need, demand, and utilization of health services needs to be further explored in the future. Secondly, differences in population base, healthcare levels, and economic incomes across cities may lead to differences in the results. Secondly, a key methodological assumption is the additivity of health effects from PM 2.5 and O 3 exposure. Our assessment calculated impacts for each pollutant independently and summed them. While pragmatic and widely used in integrated assessment models, this may not fully capture potential non-linear interactions (synergistic or antagonistic) between pollutants, as suggested by some time-series studies on mortality [ 10 ]. This could lead to an over- or underestimation of the total composite burden. Future research should aim to develop and incorporate co-exposure risk functions into health impact assessments to reduce this uncertainty. Furthermore, a significant source of uncertainty stems from the unique conditions of the study period itself, particularly the inclusion of the year 2020. The COVID-19 pandemic constituted an unplanned, global-scale intervention that drastically altered emission patterns and population exposure [ 29 ]. Although we have treated 2020 as a unique baseline and focused trend analysis on 2021–2024, its inclusion in the five-year study period inevitably affects the quantification of aggregate health and economic impacts. For instance, the overall improvement from 2020 to 2024 is likely overestimated because it captures the ‘recovery’ to normal conditions rather than purely the ‘improvement’ attributable to long-term pollution control policies. Consequently, extrapolation of trends starting from 2020 should be interpreted with caution. Future studies with longer post-pandemic time series will be better positioned to delineate the true long-term trends from this anomalous event. Nevertheless, the study reflects to some extent the effects of PM 2.5 and O 3 pollution on population health and health economy in the study area. 4. Conclusions (a) From 2020 to 2024, the proportion of days exceeding the daily average concentration limits of PM 2.5 and O 3 in Henan province shows a trend of ‘one decreasing and one increasing’, and the monthly concentrations show a ‘U’-shaped distribution for PM 2.5 and a bimodal pattern with elevated levels from May to September for O 3 . PM 2.5 pollution range is shown to shrink year by year, and O 3 pollution is mainly distributed in the northeastern and central parts of Henan Province. The annual average PM 2.5 and O 3 concentrations show a decreasing and increasing trend, respectively. (b) The number of PM 2.5 and O 3 composite pollution days in Henan Province from 2020 to 2024 is generally decreasing, but still forms double peaks in March–April and September–October. Spatially, there is a pattern of ‘high in the north and low in the south, high in the east and low in the west’—the cities of Anyang, Hebi, and Luohe maintain high levels throughout the year, while the cities of Nanyang and Sanmenxia show significantly lower levels. (c) The number of premature deaths attributed to different health effects (all-cause, cardiovascular system diseases, respiratory system diseases) attributable to PM 2.5 pollution in the Henan province region in 2024 is significantly lower than that in 2020, while the number of premature deaths attributed to O 3 shows a decreasing and then an increasing trend. (d) The economic losses attributable to different health effects attributed to PM 2.5 pollution show a decreasing and then only a steady trend from 2020 to 2024, whereas the corresponding economic losses linked to O 3 pollution exhibited a fluctuating upward trajectory. (e) Should both the annual average concentrations of PM 2.5 and O 3 meet the Grade I standards of GB 3095-2012 by 2030 (PM 2.5 at 15 μg/m 3 , O 3 at 100 μg/m 3 ), the number of premature deaths attributable to PM 2.5 and O 3 in Henan Province would decrease by 83.3% and 13.5%, respectively, compared to 2020. The total economic losses from health impacts caused by these two pollutants would be reduced by RMB 65.84 billion, yielding significant economic benefits. Declarations Author Contributions: Conceptualization, R.Z. and Y.C. (Yalun Cheng); methodology, Y.C. (Yalun Cheng), L.W., and B.H.; software, L.W. and R.L.; validation, Y.C. (Yalun Cheng), Z.S., R.L., and Y.C. (Yaping Chen); formal analysis, Y.C. (Yaping Chen), K.Z., and L.W.; investigation, R.L. and L.W.; resources, B.H., K.Z., and R.Z.; data curation, Y.C. (Yaping Chen), Z.S., R.L., and Y.C. (Yaping Chen); writing—original draft preparation, Y.C. (Yalun Cheng), Z.S., R.L., and Y.C. (Yaping Chen); writing—review and editing, R.Z., B.H., and X.W.; visualization, Y.C. (Yalun Cheng)and Z.S.; supervision, R.Z., B.H., and L.W.; project administration, R.Z. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded by the Training Program for Young Backbone Teachers in Higher Education Institutions in Henan Province (2025GGJS002); the National Key Research and Development Program of China (No. 2024YFC3713700); and the Basic Research (Natural Science) Cultivation Project of Zhengzhou University (No. JC23410024). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the sponsors. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: The data presented in this study are openly available at https://sthjt.henan.gov.cn/hjzsbz/ (accessed on 10 February 2026). 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Globalestimatesofmortalityassociatedwithlong-termexposuretooutdoorfineparticulatematter. Proc. Natl. Acad. Sci. USA 2018 , 115 ,9592–9597.https://doi.org/10.1073/pnas.1803222115. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Apr, 2026 Reviewers agreed at journal 23 Mar, 2026 Reviewers agreed at journal 22 Mar, 2026 Reviewers agreed at journal 16 Feb, 2026 Reviewers invited by journal 11 Feb, 2026 Editor assigned by journal 11 Feb, 2026 Editor invited by journal 11 Feb, 2026 Submission checks completed at journal 10 Feb, 2026 First submitted to journal 10 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-8807618","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":591901365,"identity":"78b525c6-1572-4bea-a89e-5a5f95c1e5e5","order_by":0,"name":"Liangkui Wei","email":"","orcid":"","institution":"The Third Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Liangkui","middleName":"","lastName":"Wei","suffix":""},{"id":591901368,"identity":"2b8be168-78b0-4f02-a85a-eb83ee335e05","order_by":1,"name":"Yalun Cheng","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yalun","middleName":"","lastName":"Cheng","suffix":""},{"id":591901370,"identity":"7fbf72f3-cdbd-4b5c-ae94-fe2cc8af9490","order_by":2,"name":"Dan Wang","email":"","orcid":"","institution":"The Third Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Wang","suffix":""},{"id":591901371,"identity":"86e33beb-27da-456a-856e-4f196156fed8","order_by":3,"name":"Zhengke Si","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Zhengke","middleName":"","lastName":"Si","suffix":""},{"id":591901372,"identity":"17043e4c-8057-4c7a-ac74-cc3830a4e4ce","order_by":4,"name":"Ruotong Liu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruotong","middleName":"","lastName":"Liu","suffix":""},{"id":591901373,"identity":"ee2b0ba1-abdb-467d-b49f-b298fa4201d4","order_by":5,"name":"Kai Zhou","email":"","orcid":"","institution":"Zhengzhou Ecological Environment Monitoring Center","correspondingAuthor":false,"prefix":"","firstName":"Kai","middleName":"","lastName":"Zhou","suffix":""},{"id":591901374,"identity":"b7995c5b-ee78-4caf-a859-775ab0c85a27","order_by":6,"name":"Pengliang Wang","email":"data:image/png;base64,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","orcid":"","institution":"The Third Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Pengliang","middleName":"","lastName":"Wang","suffix":""},{"id":591901375,"identity":"d054da2e-cd9c-4f49-8091-bb7468121070","order_by":7,"name":"Bingxin Hu","email":"","orcid":"","institution":"Institute of Atmospheric Environment, Chinese Research Academy of Environmental Sciences","correspondingAuthor":false,"prefix":"","firstName":"Bingxin","middleName":"","lastName":"Hu","suffix":""},{"id":591901376,"identity":"ca0ae85e-c913-4248-b46f-456adc3d3510","order_by":8,"name":"Rencheng Zhu","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Rencheng","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2026-02-06 13:25:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8807618/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8807618/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102837912,"identity":"65786c75-265e-4434-8ebc-f228ad2260ca","added_by":"auto","created_at":"2026-02-17 11:26:28","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2518866,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e at different time scales in Henan Province from 2020 to 2024.(\u003cstrong\u003ea\u003c/strong\u003e) Annual distribution of PM\u003csub\u003e2.5\u003c/sub\u003e daily average days percentage by concentration ranges; (\u003cstrong\u003eb\u003c/strong\u003e) Annual distribution of O\u003csub\u003e3\u003c/sub\u003e daily average days percentage by concentration ranges; (\u003cstrong\u003ec\u003c/strong\u003e) Monthly PM\u003csub\u003e2.5\u003c/sub\u003e concentration trends, including the 4-year average and annual data for 2020–2024; (\u003cstrong\u003ed\u003c/strong\u003e) Monthly O\u003csub\u003e3\u003c/sub\u003e concentration trends, including the 4-year average and annual data for 2020–2024.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/b699ee9c3c01c40b36c7909f.png"},{"id":102837909,"identity":"dc0a839a-8817-4c06-89b1-43388845c160","added_by":"auto","created_at":"2026-02-17 11:26:27","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":345986,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of PM2.5 and O3 across cities in Henan Province from 2021 to 2024.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/ababda2e04edc1d4ca361b02.png"},{"id":102837921,"identity":"3be24ac6-6ffc-41b2-b929-552e1621086c","added_by":"auto","created_at":"2026-02-17 11:26:29","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":95371,"visible":true,"origin":"","legend":"\u003cp\u003eCharacteristics of combined PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution days at multiple monitoring stations across 17 cities in Henan Province from 2020 to 2024. (\u003cstrong\u003ea\u003c/strong\u003e) Annual variation of composite pollution days from 2020 to 2024; columns represent the total composite pollution days per year, while lines correspond to the monthly trend of composite pollution days for each year; (\u003cstrong\u003eb\u003c/strong\u003e) Spatial distribution of pollution levels in the study area, with different colors indicating specific pollution level values; the scale bar denotes geographic distance (0–80 km), and the north-pointing compass (N icon) marks the direction.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/e36a3bc188e0f35d90e05dbb.png"},{"id":102837911,"identity":"5b7f4f1b-1265-45de-9e32-e02bb0eebde5","added_by":"auto","created_at":"2026-02-17 11:26:28","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1659781,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of premature deaths attributed to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution in Henan Province from 2020 to 2024. (\u003cstrong\u003ea\u003c/strong\u003e) Premature deaths attributed to PM\u003csub\u003e2.5\u003c/sub\u003e pollution, categorized by three health outcomes (all-cause, cardiovascular system, respiratory system) across 2020–2024 (different colors correspond to respective years, and error bars indicate data variability); (\u003cstrong\u003eb\u003c/strong\u003e) Premature deaths attributed to O\u003csub\u003e3\u003c/sub\u003e pollution, covering the same three health outcomes (all-cause, cardiovascular system, respiratory system) over 2020–2024 (consistent with subfigure (\u003cstrong\u003ea\u003c/strong\u003e) in year color coding and error bar representation).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/9ca8dfa05efd9310fc543c2b.png"},{"id":102837910,"identity":"80af8fed-d0c9-4e4d-8bd3-22aa642893c9","added_by":"auto","created_at":"2026-02-17 11:26:28","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3944512,"visible":true,"origin":"","legend":"\u003cp\u003eThe number of premature deaths attributed to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution in cities of Henan Province from 2020 to 2024.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/c8e0f38428f5839157411fe7.png"},{"id":102837922,"identity":"1bdfef71-287e-40c0-8225-a95e6b7b3e8c","added_by":"auto","created_at":"2026-02-17 11:26:29","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3909381,"visible":true,"origin":"","legend":"\u003cp\u003eEconomic losses attributed to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution in cities of Henan Province from 2020 to 2024.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/f02af85447fd4cfab650cd6a.png"},{"id":103056425,"identity":"115472fd-a3a9-4949-9c98-8610f78694ff","added_by":"auto","created_at":"2026-02-20 09:10:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13637532,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8807618/v1/8b8d0687-7d6e-457f-b989-29ad77010417.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of the Health and Economic Benefits of PM 2.5 -O 3 Composite Pollution in Henan Province, China, 2020–2024","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAs China\u0026rsquo;s urbanization process continues to advance, the composition of air pollutants is shifting from a single soot type to a combination of various pollutants, resulting in composite pollution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This transition is particularly evident in the cases of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e composite pollutants, which have become increasingly prominent. With the promulgation and implementation of the Action Plan for Air Pollution Prevention and Control and other measures, PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in China\u0026rsquo;s cities have shown a marked decline. However, the concentration is still much higher than the targets set out in the World Health Organization\u0026rsquo;s \u0026lsquo;Global Air Quality Guidelines\u0026rsquo; [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. At the same time, O\u003csub\u003e3\u003c/sub\u003e pollution has become increasingly severe, especially in major urban agglomerations such as the Central Plains, Beijing-Tianjin-Hebei, and the Yangtze River Delta, where O\u003csub\u003e3\u003c/sub\u003e concentrations are 25% to 40% higher than in other regions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHuman exposure to polluted air, especially heavily polluted air, has been demonstrated to have a detrimental effect on the cardiovascular, respiratory, and immune systems [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Air pollution has become one of the major health risk factors worldwide. For instance, the Global Air Quality Report 2024 asserts that air pollution was the fourth most significant global mortality risk factor in 2019, resulting in approximately 6.7\u0026nbsp;million deaths due to long-term exposure-related risks. Furthermore, it is reported that more than 90% of the global population still resides in areas with high levels of air pollution. This has led to scholars in various countries paying continuous attention to the health effects of air pollution. Numerous studies have confirmed that atmospheric PM\u003csub\u003e2.5\u003c/sub\u003e is a key health killer, and long-term exposure significantly increases the risk of chronic obstructive pulmonary disease (COPD), stroke, lung cancer, lower respiratory tract infections, and ischemic heart disease. The risk of cardiovascular deaths increases by 6\u0026ndash;8% for every 10 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e increase in concentration [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]; at the same time, as a strong oxidant, O\u003csub\u003e3\u003c/sub\u003e, once inhaled, will rapidly attack the respiratory epithelial cells, leading to cell membrane rupture and cell death, and disrupting lung barrier function [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Ozone also irritates the eyes and respiratory tract, and has an effect on the central nervous system, leading to impaired vigilance and performance [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Based on the analysis of time-series data from 372 cities worldwide, we found that PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e had synergistic additive effects on all-cause, cardiovascular, and respiratory deaths (synergistic indices 1.4\u0026ndash;2), and the number of premature deaths caused by their combined exposure was much greater than the sum of separate exposures [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHenan Province, a substantial agricultural region and a major demographic hub in China, provides a critical context for studying composite pollution. With a population of 98.2\u0026nbsp;million at the end of 2024, ranking third in the country, its high population density increases the public health burden of air pollution. The agricultural activities, particularly biomass burning and ammonia emissions from fertilizer use, are significant precursors for secondary PM\u003csub\u003e2.5\u003c/sub\u003e formation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Concurrently, the province\u0026rsquo;s intensive industrial and urban development exacerbates local emissions. In Henan, the primary sources of PM\u003csub\u003e2.5\u003c/sub\u003e include industrial emissions (e.g., steel, coking, and cement production), coal combustion, vehicle exhaust, and agricultural residue burning [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. O\u003csub\u003e3\u003c/sub\u003e is primarily generated through photochemical reactions involving NO\u003csub\u003ex\u003c/sub\u003e and VOCs, emitted largely from transportation, industrial processes, and solvent use [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The northern and central regions, such as Zhengzhou and Anyang, exhibit higher emissions due to concentrated industrial and traffic activities, consistent with their higher observed pollutant concentrations [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Given these diverse and intense emission sources, it is unsurprising that Henan Province ranks low among 168 key cities in the country for air quality. Eight cities (Xinxiang, Anyang, Hebi, Jiaozuo, Pingdingshan, Luoyang, Zhengzhou, and Xuchang) make up the bottom twenty in 2024, accounting for the largest proportion of cities. Xinxiang ranks in the bottom three in the country [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and is listed as a priority for PM\u003csub\u003e2.5\u003c/sub\u003e-O\u003csub\u003e3\u003c/sub\u003e synergistic management by the State Council. Based on the 2020\u0026ndash;2024 national ambient air quality monitoring station data from 17 cities in Henan Province, this study used the environmental health effect assessment method to investigate the spatial and temporal distribution characteristics of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution and the health impacts and economic losses caused by pollution exposure in Henan Province, with a view to providing references for the development of air pollution prevention and health protection measures in Henan Province. Despite numerous studies on air pollution in China, few have simultaneously assessed the health and economic impacts of both PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e in a highly industrialized and agriculturally active region like Henan. Moreover, existing studies often neglect the compounding effects of these pollutants and their interactive health burdens [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This study fills this gap by integrating high-resolution monitoring data, advanced health impact assessment modeling, and scenario analysis to provide actionable insights for regional air quality management, building on the foundational approaches of prior burden-of-disease studies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data Sources and Processing\u003c/h2\u003e \u003cp\u003eHourly PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentration data (1 January 2020\u0026ndash;31 December 2024) for 17 cities in Henan Province (except Ji yuan) were obtained from the National Urban Air Quality Real-Time Dissemination Platform of the China Environmental Monitoring General Station (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://air.cnemc.cn:18007\u003c/span\u003e\u003cspan address=\"https://air.cnemc.cn:18007\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e, accessed on 30 November 2025). The population data of all cities in Henan Province was obtained from the statistical yearbooks of each city. Per capita disposable income data was obtained from the Henan Provincial Bureau of Statistics. Baseline mortality rates for different health effect endpoints, such as all-cause premature deaths (A00-Y98), premature deaths of the cardiovascular system (I00-I99), and premature deaths of the respiratory system (J00-J99), were obtained from the China Health and Wellness Statistical Yearbook (Volume 2023).\u003c/p\u003e \u003cp\u003eWe applied the China National Environmental Monitoring Centre\u0026rsquo;s standard procedures to remove outliers and ensure consistency across stations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Data completeness was ensured by requiring at least 75% valid hourly records per day for inclusion in daily averages [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The daily average O\u003csub\u003e3\u003c/sub\u003e concentration was calculated using the maximum 8 h moving average (O\u003csub\u003e3\u003c/sub\u003e_8h_max), and the 90% percentile of the daily O\u003csub\u003e3\u003c/sub\u003e_8h_max concentration was used for the annual evaluation value of O\u003csub\u003e3\u003c/sub\u003e concentration. According to the Chinese National Ambient Air Quality Standard (GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], a pollution day was defined as having daily PM\u003csub\u003e2.5\u003c/sub\u003e concentrations exceeding 35 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e or daily maximum 8 h average O\u003csub\u003e3\u003c/sub\u003e concentrations exceeding 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e; concurrent exceedances of both thresholds were classified as compound pollution [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The adoption of the 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e O\u003csub\u003e3\u003c/sub\u003e threshold aligns with China\u0026rsquo;s regulatory framework and ensures direct policy relevance to Henan Province, although it differs from the stricter WHO guideline of 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (daily maximum 8 h average) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and recommendations in other international studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This approach follows the established methodology for assessing composite pollution in China\u0026rsquo;s regulatory context [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Consideration of the COVID-19 Impact on 2020 Data\u003c/h2\u003e \u003cp\u003eIt is important to note that the year 2020 was significantly affected by the COVID-19 pandemic, which led to widespread lockdowns and a substantial reduction in economic and social activities. This resulted in anomalously low levels of air pollution, particularly during the first half of the year [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. While the data for 2020 are included in our study to provide a complete temporal picture, its unique nature is acknowledged. In the trend analysis, 2020 is treated as a unique baseline year, and discussions of interannual trends primarily focus on the period from 2021 to 2024 to avoid potential bias from this anomalous period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Population Health Effect Assessment Methods\u003c/h2\u003e \u003cp\u003eThe number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution in cities in Henan Province from 2020 to 2024 was estimated using Benmap-CE 1.5. This was based on PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e exposure data, baseline mortality, population data, and exposure\u0026ndash;response relationship coefficients [see Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)] [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The health effect endpoints selected for this study included all-cause premature death, premature death due to respiratory system diseases, and premature death due to cardiovascular system diseases [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{Y}_{i}={Y}_{0}\\left(1-{e}^{-\\beta\\:\\times\\:{X}_{i}}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe baseline concentration of PM\u003csub\u003e2.5\u003c/sub\u003e adopts GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 \u0026lsquo;Ambient Air Quality Standards\u0026rsquo; Class I standard limit value (15 \u0026micro;g/m\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e, O\u003csub\u003e3\u003c/sub\u003e baseline concentration takes the value of 70 \u0026micro;g/m\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e];\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\beta\\:\\)\u003c/span\u003e\u003c/span\u003e is the exposure\u0026ndash;response relationship coefficient (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which indicates the percentage increase in the risk of death for different health effect endpoints for each 10 \u0026micro;g/m\u003csup\u003e\u003cb\u003e3\u003c/b\u003e\u003c/sup\u003e increase in PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations; e is a natural constant The exposure\u0026ndash;response coefficients in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e are validated for health impact assessments in China [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], having been derived from large-scale epidemiological studies in China and comparable regions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Their established application in central China, which accounts for demographic factors, supports their use for the Henan context [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The total economic loss attributable to air pollution was obtained by summing the separately calculated losses for PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e. This approach aligns with the health impact assessment framework and assumes no economic interaction between the losses caused by the two pollutants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExposure response relationship coefficients of different health outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePollutant\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth Effects Endpoint\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\varvec{\\beta\\:}\\)\u003c/span\u003e\u003c/span\u003e (Exposure\u0026ndash;Response Relationship Coefficient)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll because of an early death.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021 76 (0.013 90, 0.029 56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremature death from diseases of the cardiovascular system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.021 76 (0.009 95, 0.034 40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremature death from respiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.088 93 (0.058 27, 0.119 56)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll because of an early death.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008 10 (0.005 08, 0.011 10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremature death of the cardiovascular system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.011 10 (0.002 04, 0.020 50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremature death from respiratory diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.014 59 (0.005 58, 0.023 43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Methods for Assessing Economic Losses from Health Effects\u003c/h2\u003e \u003cp\u003eThe monetary value of a statistical life (VSL) is utilized to quantify the financial losses incurred as a result of premature mortality. The VSL is a metric of the marginal willingness to pay for the avoidance of the risk of death [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In accordance with the findings of preceding studies [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], the baseline was set at RMB 168 \u0026times; 104. The VSL was adjusted by correcting the per capita disposable income of 17 cities in Henan Province from 2020 to 2024, and the formula was calculated as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:VS{L}_{j,n}={VSL}_{base}\\times\\:({\\frac{{I}_{j,n}}{{I}_{base}})}^{m}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:VS{L}_{j,n}\\)\u003c/span\u003e\u003c/span\u003e is the statistical life value of city j in the nth year, RMB; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{VSL}_{base}\\)\u003c/span\u003e\u003c/span\u003eis the benchmark life value RMB; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{j,n}\\)\u003c/span\u003e\u003c/span\u003e is the per capita disposable income of city j in Henan province in the nth year, RMB; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{I}_{base}\\)\u003c/span\u003e\u003c/span\u003e is the corresponding per capita disposable income of the benchmark VSL, RMB; m is the coefficient of income elasticity, which is taken as the value of 1. The total economic loss attributable to air pollution was obtained by summing the separately calculated losses for PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e. This approach aligns with the health impact assessment framework and assumes no economic interaction between the losses caused by the two pollutants. This approach assumes independent effects (i.e., additivity). While some epidemiological studies, such as the analysis by Liu et al. (2023) [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], suggest potential synergistic or antagonistic interactions between PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e, quantitative exposure\u0026ndash;response relationships for their combined effects are not yet well-established for long-term mortality endpoints. Therefore, the independent-effect additive model provides a conservative and commonly used baseline estimate, consistent with approaches in studies like Anenberg et al. (2010) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The implications of this assumption are further discussed in the Uncertainty Analysis section.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Calculation of Health Risks and Economic Losses in the Target Year Scenario\u003c/h2\u003e \u003cp\u003eA future target year (2030) scenario was established in order to estimate the number of premature deaths caused by air pollution in the coming year. This estimation was made according to the methodology outlined in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e2.3\u003c/span\u003e. In addition, the economic losses that can be avoided by achieving the target concentrations in the future were also considered. The promulgation and implementation of national policies for the prevention and control of pollution is expected to result in a further reduction in the concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e in the future. In order to assess the number of premature deaths that can be avoided by decreasing the concentrations, as well as the economic benefits that would ensue from such decreases, the target scenarios for PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations in 2030 are set to have two values. Furthermore, the PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations in future years are to be reduced to the target concentrations. The resulting health and economic benefits can then be calculated based on the difference between the target scenarios and the baseline scenarios (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTarget emission reduction scenario forecast for Henan Province in 2030.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTarget Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eConcentration\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1T-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:35\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e (GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 secondary standard limit)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e100 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e (GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 Class I standard limit)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1T-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:15\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: If the PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations in the study cities do not exceed the target scenario concentrations in 2024, the values for that year are used as the target concentrations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Consideration of Meteorological Influences\u003c/h2\u003e \u003cp\u003eWe acknowledge that meteorological conditions can significantly influence pollutant concentrations. Due to the lack of a complete and consistent daily meteorological dataset across all 17 cities, we did not perform meteorological adjustment on the reported concentrations.\u003c/p\u003e \u003cp\u003eTo ensure a robust interpretation of the trends, we focused the core analysis on the post-pandemic period (2021\u0026ndash;2024) to minimize the impact of anomalous conditions in 2020. Furthermore, the opposing trends of declining PM\u003csub\u003e2.5\u003c/sub\u003e and rising O\u003csub\u003e3\u003c/sub\u003e observed in this study are consistent with findings from other studies in the North China Plain that have employed meteorological normalization [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. This consistency strengthens the inference that the dominant trends are primarily driven by emission changes rather than meteorological variability.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Characteristics of Spatial and Temporal Variations of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the changes in average concentrations of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e across 17 cities in Henan Province during different time scales from 2020 to 2024. The annual average PM\u003csub\u003e2.5\u003c/sub\u003e concentration in 2020 established an anomalously low baseline (52 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) due to the COVID-19 lockdowns [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Following the resumption of economic activities in 2021, PM\u003csub\u003e2.5\u003c/sub\u003e levels subsequently exhibited a general declining trend through 2024 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). In contrast, O\u003csub\u003e3\u003c/sub\u003e concentrations demonstrated a clear and fluctuating upward trajectory over the 2021\u0026ndash;2024 period (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), solidifying its role as the primary pollutant. This divergent trend is aligned with findings from other meteorologically adjusted studies in this region [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], suggesting that emission patterns and chemical feedbacks, rather than meteorology, are the dominant drivers. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea,b, PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e exhibited opposing trends from 2020 to 2024. Contrasted with the GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 secondary standard, the proportion of exceedance days for PM\u003csub\u003e2.5\u003c/sub\u003e (75 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e) decreased from 19.0% to 16.19%, while that for O\u003csub\u003e3\u003c/sub\u003e (160 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}\\text{g}/{\\text{m}}^{3}\\)\u003c/span\u003e\u003c/span\u003e) increased from 11.80% to 15.27%. Since 2022, the number of days on which O\u003csub\u003e3\u003c/sub\u003e has exceeded the standard has been consistently higher than the number of days on which PM\u003csub\u003e2.5\u003c/sub\u003e has done so. This has resulted in O\u003csub\u003e3\u003c/sub\u003e becoming the primary pollution factor. From Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec, it can be seen that the PM\u003csub\u003e2.5\u003c/sub\u003e monthly average concentration of the lowest value occurred in July\u0026ndash;August, showing a typical \u0026lsquo;U\u0026rsquo; distribution, which is mainly attributed to the enhanced turbulent mixing in summer to promote pollutant dilution and diffusion, so that the PM\u003csub\u003e2.5\u003c/sub\u003e concentration is reduced [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In addition, Henan\u0026rsquo;s winter PM\u003csub\u003e2.5\u003c/sub\u003e concentration is significantly higher than that in other seasons, which is closely related to its emission level, the inversion layer, and static weather [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, the monthly concentration of O\u003csub\u003e3\u003c/sub\u003e exhibits a bimodal seasonal pattern with the primary peak occurring in May-June. Following a slight dip in July, concentrations rise again to form a secondary peak in August-September. However, statistical analysis (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) indicates that the differences in O\u003csub\u003e3\u003c/sub\u003e concentrations among July, August, and September are not significant. Therefore, the observed dip in July does not constitute a statistically robust trough, and the pattern is more accurately described as a prolonged period of elevated O\u003csub\u003e3\u003c/sub\u003e levels from late spring through early autumn, with the highest concentrations in late spring (May-June). The main reason is that the two periods of May-June and August-September satisfy the high temperature, strong light, and low humidity conditions required for O\u003csub\u003e3\u003c/sub\u003e generation, and the concentration falls back in July due to the increase in cloudiness and precipitation frequency, and the weakening of solar radiation. Furthermore, for every 1 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e reduction in Henan\u0026rsquo;s annual average PM\u003csub\u003e2.5\u003c/sub\u003e concentration between 2020 and 2024, indirect increases in O\u003csub\u003e3\u003c/sub\u003e levels of approximately 0.3\u0026ndash;0.4 \u0026micro;g m\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e may occur. This is achieved by reducing aerosol surface light absorption and enhancing HO\u003csub\u003e2\u003c/sub\u003e radical production, consistent with existing chemical transport simulation results for the North China Plain [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Concurrently, the VOC/NO\u003csub\u003ex\u003c/sub\u003e ratio in northern cities (Anyang, Hebi) has fallen below 4, placing them within NO\u003csub\u003ex\u003c/sub\u003e control zones, as observed in regional studies [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Continued unilateral NO\u003csub\u003ex\u003c/sub\u003e reduction could trigger an O\u003csub\u003e3\u003c/sub\u003e rebound. In summary, the O\u003csub\u003e3\u003c/sub\u003e increase resulted from the combined effects of \u0026lsquo;changes in precursor emissions\u0026thinsp;+\u0026thinsp;enhanced photolysis due to PM\u003csub\u003e2.5\u003c/sub\u003e decline\u0026thinsp;+\u0026thinsp;high-temperature drought conditions\u0026rsquo;. Future efforts should focus on coordinated control of VOCs and NO\u003csub\u003ex\u003c/sub\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] while paying attention to the catalytic role of extreme weather events.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the changes in the average PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations in Henan Province at different time scales from 2020 to 2024, showing that from 2021 to 2024, the PM\u003csub\u003e2.5\u003c/sub\u003e concentration ranges from 40 to 55 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, presenting a band distribution of \u0026lsquo;high in the north and low in the south\u0026rsquo;. The area with a concentration of \u0026gt;\u0026thinsp;50 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e has been observed to decrease in size on an annual basis, with only evident residuals remaining in the northern region of Henan and the central portion of Henan Province. This spatial pattern is firstly closely related to regional transport, as the prevailing north wind in winter transports PM\u003csub\u003e2.5\u003c/sub\u003e polluted air masses to Henan Province, resulting in higher PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in the northern part of Henan Province than in the southern part [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, a single regional transport system is not sufficient to fully explain the persistence and spatial agglomeration of pollution, and local emissions provide an important basis for pollution formation. North and central Henan Province are the traditional industrial and energy core areas, with a concentration of heavy industries such as iron and steel, coking, and building material production, and their high-intensity local emissions, in addition to external transmission, significantly increase the pollution load. Ozone concentrations in areas of high concentration ranged from 100 to 115 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, extending from northern and central Henan to the east. The O\u003csub\u003e3\u003c/sub\u003e pollution observed in these regions is attributable to external factors, such as the transportation of pollutants from North China and the transportation of O\u003csub\u003e3\u003c/sub\u003e by summer southerly winds within the province. Additionally, the emission of substantial quantities of VOCs and NO\u003csub\u003ex\u003c/sub\u003e by traffic in cities such as Heavy Industry in northeastern Henan and Zhengzhou in central Henan serve as sufficient precursors for O\u003csub\u003e3\u003c/sub\u003e generation [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This spatial disparity is strongly supported by emission inventory and source apportionment studies. The observed pattern aligns with regional emission inventories which identify northern and central Henan as hotspots for industrial production (e.g., steel, coking), energy consumption, and high-density vehicular traffic [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Model-based source apportionment studies further confirm that regional transport from the North China Plain contributes significantly to the PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e levels in these regions [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The lower pollution levels in southern and western Henan are thus attributable to a combination of lower local industrial emissions and reduced influence from inter-regional transport.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e Composite Pollution Characteristics\u003c/h2\u003e \u003cp\u003eAs demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the figure plots the number of days of compound pollution and monthly change statistics in Henan Province from 2020 to 2024. The data on the number of days of composite pollution in Henan Province from 2020 to 2024 demonstrates a downward trend, followed by an upward shift. In 2020, the number of days fell to 83. However, due to the suppression of economic and social activities in 2021 as a result of the epidemic, this figure fell to a minimum of 64 days [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], subsequent to this, there was a marked recovery of the economy and society as a whole, which resulted in a significant increase in the number of days between 2022 and 2024, reaching a peak of 96 days. The primary cause of this rebound trend is the growing issue of O\u003csub\u003e3\u003c/sub\u003e pollution, in the context of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations that have not yet been adequately addressed, resulting in an extended period of overlapping compound pollution. The monthly distribution displays a \u0026lsquo;double-peak\u0026rsquo; pattern, with March\u0026ndash;April and September\u0026ndash;October being the periods with the highest incidence. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, composite pollution in Henan Province exhibits a pattern of \u0026lsquo;higher levels in the north and lower in the south, higher in the east and lower in the west\u0026rsquo;; cities in the central-northern region\u0026mdash;Anyang, Hebi, and Luohe\u0026mdash;consistently experience high pollution levels throughout the year. In contrast, the frequency and intensity of composite pollution episodes in Nanyang in the south and Sanmenxia in the west are significantly lower than those in the central-northern cities. The distinct regional differences are clearly discernible in the spatial distribution.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Disease Burden Assessment of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e Pollution\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the alteration in the number of premature fatalities attributable to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e contamination in 17 cities within Henan Province from 2020 to 2024. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, the annual average PM\u003csub\u003e2.5\u003c/sub\u003e concentration in 17 cities in Henan Province exhibited a marginal decline during the study period. It is critical to contextualize these reductions relative to the 2020 baseline. The significant decline is partly attributable to the rebound of economic activities and population exposure to pre-pandemic levels [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], superimposed on the long-term effectiveness of pollution control measures [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Therefore, the trend from 2021 to 2024 provides a more reliable assessment of the genuine improvement in air pollution-related health impacts. The number of premature deaths from all causes, premature deaths from respiratory diseases, and premature deaths from cardiovascular diseases attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution in the province in 2024 was divided into 48,169, 14,750, and 22,807 deaths, which were reduced by 12.0%, 10.6%, and 12.0%, respectively, compared with those in 2020. This result shows that Henan has taken many measures to reduce PM\u003csub\u003e2.5\u003c/sub\u003e and significantly cut down the health risk, and the effectiveness of pollution control is obvious. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e indicates that the overall number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e across 17 cities declined between 2020 and 2024. Zhengzhou recorded the highest absolute number of premature deaths due to its greatest population density, followed by Nanyang and Zhoukou. Conversely, although Anyang and Puyang have smaller populations, high pollution concentrations resulted in a relatively high burden. Consequently, future efforts should focus on high-density population areas to strengthen emission reductions and exposure controls, thereby further mitigating health impacts.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb shows that O\u003csub\u003e3\u003c/sub\u003e concentrations in 17 cities in Henan Province showed a decreasing and then increasing trend during the study period, and the number of premature deaths attributable to all-causes, respiratory diseases and cardiovascular diseases in 2024 were 20,577, 3090, and 13,269, respectively, which were increased by 22.0%, 21.8% and 21.9% compared with those in 2020. As demonstrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the cities with the highest number of premature deaths attributable to O\u003csub\u003e3\u003c/sub\u003e pollution are Zhengzhou, Anyang, and Zhoukou. In 2024, the cities demonstrating the most substantial escalation in premature fatalities attributable to O\u003csub\u003e3\u003c/sub\u003e contamination in comparison to 2023 are Shangqiu, Xinxiang, and Luoyang. It is imperative that the endeavors to prevent and control O\u003csub\u003e3\u003c/sub\u003e pollution in these regions remain steadfast. In recent years, with the implementation of a series of low-carbon and emission reduction measures, PM\u003csub\u003e2.5\u003c/sub\u003e pollution levels in different regions have decreased significantly [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], so the number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e has decreased, but the number of premature deaths attributable to O\u003csub\u003e3\u003c/sub\u003e has shown a rising trend [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. From the comparison of the disease burden of premature deaths between the two, the number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e in 17 cities in Henan Province in 2024 was about 2.5 times that of O\u003csub\u003e3\u003c/sub\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Assessment of Economic Losses of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e Pollution\u003c/h2\u003e \u003cp\u003eThe economic losses of all-cause, respiratory diseases and early deaths from cardiovascular diseases attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution in Henan Province in 2020\u0026ndash;2024 show a decreasing and then stable trend, and the economic losses of the above health effects in 2024 are RMB 89.8\u0026nbsp;billion, RMB 27.5\u0026nbsp;billion, and RMB 42.5\u0026nbsp;billion, respectively, which are compared to 2020 and have, respectively, decreased by 11.5%, 13.3%, and 11.5%. From Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e the economic losses attributed to different health effects of PM\u003csub\u003e2.5\u003c/sub\u003e pollution in 17 cities in Henan Province showed an overall decreasing trend, and the cities with higher economic losses during the study period were Zhengzhou City, Nanyang City, and Luoyang City, which is related to the higher population density and economic development level of the cities [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe economic losses attributed to different health effects of O\u003csub\u003e3\u003c/sub\u003e pollution in 17 cities of Henan Province in 2020\u0026ndash;2024 showed a decreasing and then increasing trend, and the economic losses of all-cause, respiratory diseases and cardiovascular diseases were RMB 38.8\u0026nbsp;billion, RMB 5.8\u0026nbsp;billion, and RMB 25.0\u0026nbsp;billion in 2024, respectively, which were 55.4%, 55.0% and 55.2%, respectively, higher in comparison with 2020. From Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, the economic losses attributed to different health effects of O\u003csub\u003e3\u003c/sub\u003e pollution generally show a fluctuating upward trend, similar to PM\u003csub\u003e2.5\u003c/sub\u003e pollution, and the city with the largest economic loss attributed to O\u003csub\u003e3\u003c/sub\u003e pollution is Zhengzhou City, followed by Anyang City and Xinxiang City.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe above study illustrates that the health and economic losses caused by PM\u003csub\u003e2.5\u003c/sub\u003e pollution to 17 cities in Henan Province have gradually stabilized in recent years, while the health and economic losses caused by O\u003csub\u003e3\u003c/sub\u003e pollution to 17 cities in Henan Province have shown a gradual increase in recent years. The number of premature deaths and health and economic losses caused by O\u003csub\u003e3\u003c/sub\u003e pollution have further intensified in a heavily polluted environment, and, in particular, the significant increase in health risks and economic losses caused by cardiovascular system diseases deserves further attention. Therefore, strengthening the synergistic management of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution to improve the health of the population is the focus of future environmental health efforts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5. Scenario Projections\u003c/h2\u003e \u003cp\u003eIn recent years, the government has taken a number of effective management measures to reduce the concentration of PM\u003csub\u003e2.5\u003c/sub\u003e pollutants, and according to the relevant plan, China\u0026rsquo;s PM\u003csub\u003e2.5\u003c/sub\u003e concentration is expected to reach the IT-1 standard (35 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e) by 2030 [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the health benefits and health economic benefits under different scenarios in 2030. By then, if PM\u003csub\u003e2.5\u003c/sub\u003e concentrations in cities in Henan Province were reduced to this standard, the number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution would be reduced by 8896, or 18.5%; however, the health benefits of lower PM\u003csub\u003e2.5\u003c/sub\u003e concentrations are more limited due to the increasing trend of population aging and the implementation of fertility stimulation measures. Some scholars estimates for eastern and central China show that while PM\u003csub\u003e2.5\u003c/sub\u003e-attributable deaths declined by 14.4% from 2013 to 2017, population aging led to a widening of the elderly population base, which offset some of the health benefits-specifically, population aging contributed\u0026thinsp;\u0026minus;\u0026thinsp;12.4% to the health burden (that is, the exacerbating the burden), while the contribution of PM\u003csub\u003e2.5\u003c/sub\u003e concentration reduction was +\u0026thinsp;14.6% [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Therefore, further reductions in PM\u003csub\u003e2.5\u003c/sub\u003e target concentrations are needed. In the event of Henan Province\u0026rsquo;s PM\u003csub\u003e2.5\u003c/sub\u003e concentration attaining the IT-2 scenario (GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 Class I standard limit), the number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution would be reduced by 40,143 individuals, representing an 83.3% decrease. The health benefits would be exceptionally significant.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHealth benefits and health economic benefits under different scenarios in 2030.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProject\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBaseline Scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIT-1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIT-2\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of premature deaths attributed to PM\u003csub\u003e2.5\u003c/sub\u003e/person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48,169 (31,216, 64,512)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39,273 (25,333, 52,838)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8026 (5137, 10,882)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth economic losses attributed to PM\u003csub\u003e2.5\u003c/sub\u003e/(10\u003csup\u003e8\u003c/sup\u003e RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e897.6 (581.7, 1202.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e953.2 (614.8, 1282.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e194.8 (124.7, 264.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of avoidable premature deaths attributed to PM\u003csub\u003e2.5\u003c/sub\u003e/person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8896 (5883, 11,674)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40,143 (26,079, 53,630)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvoidable health economic losses attributed to PM\u003csub\u003e2.5\u003c/sub\u003e/(10\u003csup\u003e8\u003c/sup\u003e RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;55.6 (\u0026minus;\u0026thinsp;33.1, \u0026minus;\u0026thinsp;80.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e702.8 (457, 938)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of premature deaths attributed to O\u003csub\u003e3\u003c/sub\u003e/person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20,577 (12,986, 28,025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17,807 (11,218, 24,293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5984 (3758, 8188)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHealth economic losses attributed to O\u003csub\u003e3\u003c/sub\u003e/(10\u003csup\u003e8\u003c/sup\u003e RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e387.8 (224.7, 528.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e432.2 (272.3, 589.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e145.2 (91.2, 198.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of avoidable premature deaths attributed to O\u003csub\u003e3\u003c/sub\u003e/person\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2770 (1768, 3732)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14,593 (9228, 19,837)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAvoidable health economic losses attributed to O\u003csub\u003e3\u003c/sub\u003e/(10\u003csup\u003e8\u003c/sup\u003e RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;47.6 (\u0026minus;\u0026thinsp;44.4, \u0026minus;\u0026thinsp;61.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e245.5 (133.5, 329.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the analysis of O\u003csub\u003e3\u003c/sub\u003e pollution trends from 2020 to 2024, O\u003csub\u003e3\u003c/sub\u003e pollution shows a fluctuating upward trend, and if the O\u003csub\u003e3\u003c/sub\u003e pollution concentration in cities in Henan Province is reduced to 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e in 2030, the number of premature deaths caused by it will be significantly reduced by 2770 (95% CI, 1768 to 3732), or about 13.5% (95% CI, 13.3% to 13.6%), compared with the baseline scenario. However, the health benefits of reduced O\u003csub\u003e3\u003c/sub\u003e concentrations are more limited. Therefore, it is necessary to set a higher emission reduction target, such as reducing the O\u003csub\u003e3\u003c/sub\u003e concentration to 80 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e, which will reduce the number of premature deaths by 14,593 (95% CI, 9228 to 19837) in 2030.\u003c/p\u003e \u003cp\u003eIn recent years, there has been a downward trend in atmospheric PM\u003csub\u003e2.5\u003c/sub\u003e exposure concentrations in Henan Province, while O\u003csub\u003e3\u003c/sub\u003e concentrations have exhibited fluctuating increases. It is posited that, based on the 2024 baseline scenario, assuming pollutant concentrations undergo a certain degree of change, reductions in exposure levels for both pollutants can yield divergent health economic benefits. Specifically, when PM\u003csub\u003e2.5\u003c/sub\u003e concentrations meet the IT-1 standard, the improvements in quality of life and increases in per capita disposable income result in an additional RMB 5.56 (95% CI, 3.31 to 8.03) billion in health-related economic losses compared to 2024 pollution levels. Consequently, the implementation of augmented emission reduction targets\u0026mdash;such as the reduction of PM\u003csub\u003e2.5\u003c/sub\u003e concentrations to the IT-2 standard\u0026mdash;will generate approximately RMB 70.3 (95% CI, 457 to 938) billion in health-related economic benefits. Similarly to PM\u003csub\u003e2.5\u003c/sub\u003e pollution, as per capita disposable income increases, when O\u003csub\u003e3\u003c/sub\u003e concentrations reach 100 \u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e (GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 Class I standard limit) by 2030, economic losses relative to the baseline scenario will rise by RMB 4.8 (95% CI, 4.4 to 6.1) billion. Should O\u003csub\u003e3\u003c/sub\u003e concentrations be reduced to the IT-2 standard, losses would decrease by approximately RMB 24.6 (95% CI, 13.3 to 33.0) billion. Overall, pollution reduction yields both health and economic benefits. As pollutant concentrations continue to decline, the marginal health benefits derived from such reductions become increasingly pronounced [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Therefore, on the basis of achieving the existing standards, it is imperative to implement higher standards of emission reduction measures, so as to reduce the health burden and economic losses caused by pollution, and further offset the negative impacts such as population aging.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.6. Comparative Analysis and Policy Implications\u003c/h2\u003e \u003cp\u003eThe distinct pollution transition observed in Henan\u0026mdash;marked by declining PM\u003csub\u003e2.5\u003c/sub\u003e but rising O\u003csub\u003e3\u003c/sub\u003e\u0026mdash;reflects a broader national pattern documented in key regions such as Beijing\u0026ndash;Tianjin\u0026ndash;Hebei and the Yangtze River Delta [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This divergence underscores that conventional emission control measures, while effective against particulate matter, are inadequate for addressing the non-linear photochemistry driving ozone formation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Consequently, a shift toward multi-pollutant synergistic control is urgently needed. Integrated strategies targeting common precursors\u0026mdash;especially NO\u003csub\u003ex\u003c/sub\u003e and VOCs\u0026mdash;deliver the greatest co-benefits [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Our scenario analysis confirms that achieving the Class I standard (IT-2) could prevent approximately 54,700 premature deaths and yield economic benefits of nearly RMB 95\u0026nbsp;billion, highlighting the significant returns from coordinated action. We thus recommend a focused policy package: accelerating the transition from coal to clean energy in northern and central Henan [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]; implementing precise VOC and NO\u003csub\u003ex\u003c/sub\u003e controls in transportation, industrial, and solvent-related sectors [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]; and strengthening regional joint prevention mechanisms with neighboring provinces to mitigate cross-boundary pollution [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Such a synergistic pathway is essential to realizing the \u0026ldquo;Beautiful China\u0026rdquo; vision and maximizing long-term health and economic co-benefits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.7. Uncertainty Analysis\u003c/h2\u003e \u003cp\u003eThe study analyzed the spatial and temporal variations, health effects, and potential health benefits of PM2.5 and O3 concentrations in 17 cities of Henan Province in the target years 2020\u0026ndash;2024, with data derived from ground-based environmental monitoring sites. Inherent limitations of such monitoring networks, including finite spatial density (potentially missing gradients within cities) and the representativeness of site locations (e.g., proximity to roads or green spaces), resulted in discrepancies between the population exposure simulation based on station data and the actual population exposure levels. A key limitation of this study is the absence of meteorological adjustment for pollutant concentrations. The observed trends may therefore be confounded by interannual meteorological variability. The limitations of the monitoring network resulted in discrepancies between the population simulation and the actual exposure levels, and further optimization of the exposure assessment is needed in the future. In the selection of health effect endpoints, only all-cause premature death, premature death of cardiovascular system diseases and premature death of respiratory system diseases, which have more research bases at present, were selected, and the possible effects of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e on other acute and chronic diseases were neglected, which underestimated the impact of the effect results to a certain extent [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The willingness-to-pay method was used as a measure in assessing the economic loss of health, ignoring the true cost of health services, and the relationship between air pollution and the need, demand, and utilization of health services needs to be further explored in the future. Secondly, differences in population base, healthcare levels, and economic incomes across cities may lead to differences in the results. Secondly, a key methodological assumption is the additivity of health effects from PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e exposure. Our assessment calculated impacts for each pollutant independently and summed them. While pragmatic and widely used in integrated assessment models, this may not fully capture potential non-linear interactions (synergistic or antagonistic) between pollutants, as suggested by some time-series studies on mortality [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This could lead to an over- or underestimation of the total composite burden. Future research should aim to develop and incorporate co-exposure risk functions into health impact assessments to reduce this uncertainty. Furthermore, a significant source of uncertainty stems from the unique conditions of the study period itself, particularly the inclusion of the year 2020. The COVID-19 pandemic constituted an unplanned, global-scale intervention that drastically altered emission patterns and population exposure [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Although we have treated 2020 as a unique baseline and focused trend analysis on 2021\u0026ndash;2024, its inclusion in the five-year study period inevitably affects the quantification of aggregate health and economic impacts. For instance, the overall improvement from 2020 to 2024 is likely overestimated because it captures the \u0026lsquo;recovery\u0026rsquo; to normal conditions rather than purely the \u0026lsquo;improvement\u0026rsquo; attributable to long-term pollution control policies. Consequently, extrapolation of trends starting from 2020 should be interpreted with caution. Future studies with longer post-pandemic time series will be better positioned to delineate the true long-term trends from this anomalous event. Nevertheless, the study reflects to some extent the effects of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution on population health and health economy in the study area.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Conclusions","content":"\u003cp\u003e(a) From 2020 to 2024, the proportion of days exceeding the daily average concentration\u0026nbsp;limits\u0026nbsp;of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e in Henan\u0026nbsp;province\u0026nbsp;shows a trend of\u0026nbsp;\u0026lsquo;one decreasing and one increasing\u0026rsquo;, and\u0026nbsp;the monthly concentrations show a \u0026lsquo;U\u0026rsquo;-shaped distribution for PM\u003csub\u003e2.5\u003c/sub\u003e and a bimodal pattern with elevated levels from May to September for O\u003csub\u003e3\u003c/sub\u003e. PM\u003csub\u003e2.5\u003c/sub\u003e pollution range is shown to shrink year by year, and O\u003csub\u003e3\u0026nbsp;\u003c/sub\u003epollution is mainly distributed in the northeastern and central parts of Henan Province.\u0026nbsp;The annual average PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e concentrations show a decreasing and increasing trend, respectively.\u003c/p\u003e\n\u003cp\u003e(b) The number of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u0026nbsp;\u003c/sub\u003ecomposite pollution days in Henan Province from 2020 to 2024 is generally decreasing, but still forms double peaks in March\u0026ndash;April and September\u0026ndash;October. Spatially, there is a pattern of\u0026nbsp;\u0026lsquo;high in the north and low in the south, high in the east and low in the west\u0026rsquo;\u0026mdash;the cities of Anyang, Hebi, and Luohe maintain high levels throughout the year, while the cities of Nanyang and Sanmenxia show significantly lower levels.\u003c/p\u003e\n\u003cp\u003e(c) The number of premature deaths attributed\u0026nbsp;to different health effects (all-cause, cardiovascular system diseases, respiratory system diseases) attributable to PM\u003csub\u003e2.5\u003c/sub\u003e pollution in the Henan province region in 2024 is significantly lower than that in 2020, while the number of premature deaths attributed\u0026nbsp;to O\u003csub\u003e3\u003c/sub\u003e shows a decreasing and then an increasing trend.\u003c/p\u003e\n\u003cp\u003e(d) The economic losses attributable to different health effects attributed\u0026nbsp;to PM\u003csub\u003e2.5\u003c/sub\u003e pollution show a decreasing and then only a steady trend from 2020 to 2024, whereas\u0026nbsp;the corresponding economic losses linked to O\u003csub\u003e3\u003c/sub\u003e pollution exhibited a fluctuating upward trajectory.\u003c/p\u003e\n\u003cp\u003e(e) Should both the annual average concentrations of PM\u003csub\u003e2.5\u0026nbsp;\u003c/sub\u003eand O\u003csub\u003e3\u003c/sub\u003e meet the Grade I standards of GB 3095-2012 by 2030 (PM\u003csub\u003e2.5\u003c/sub\u003e at 15 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e, O\u003csub\u003e3\u003c/sub\u003e at 100 \u0026mu;g/m\u003csup\u003e3\u003c/sup\u003e), the number of premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e in Henan Province would decrease by 83.3% and 13.5%, respectively, compared to 2020. The total economic losses from health impacts caused by these two pollutants would be reduced by RMB 65.84 billion, yielding significant economic benefits.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eAuthor Contributions: Conceptualization, R.Z. and Y.C. (Yalun Cheng); methodology, Y.C. (Yalun Cheng), L.W., and B.H.; software, L.W. and R.L.; validation, Y.C. (Yalun Cheng), Z.S., R.L., and Y.C. (Yaping Chen); formal analysis, Y.C. (Yaping Chen), K.Z., and L.W.; investigation, R.L. and L.W.; resources, B.H., K.Z., and R.Z.; data curation, Y.C. (Yaping Chen), Z.S., R.L., and Y.C. (Yaping Chen); writing\u0026mdash;original draft preparation, Y.C. (Yalun Cheng), Z.S., R.L., and Y.C. (Yaping Chen); writing\u0026mdash;review and editing, R.Z., B.H., and X.W.; visualization, Y.C. (Yalun Cheng)and Z.S.; supervision, R.Z., B.H., and L.W.; project administration, R.Z. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003eFunding: This work was funded by the Training Program for Young Backbone Teachers in Higher Education Institutions in Henan Province (2025GGJS002); the National Key Research and Development Program of China (No. 2024YFC3713700); and the Basic Research (Natural Science) Cultivation Project of Zhengzhou University (No. JC23410024). The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the sponsors.\u003c/p\u003e\n\u003cp\u003eInstitutional Review Board Statement: Not applicable.\u003c/p\u003e\n\u003cp\u003eInformed Consent Statement: Not applicable.\u003c/p\u003e\n\u003cp\u003eData Availability Statement: The data presented in this study are openly available at https://sthjt.henan.gov.cn/hjzsbz/ (accessed on 10 February 2026). Should the link be inaccessible, the datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003eConflicts of Interest: The authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eZhao,H.;Chen,K.;Liu,Z.;Zhang,Y.;Shao,T.;Zhang,H.CoordinatedcontrolofPM2.5andO3isurgentlyneededinChina\u003cbr\u003eafterimplementationofthe\u0026ldquo;Airpollutionpreventionandcontrolactionplan\u0026rdquo;.\u003cem\u003e\u0026nbsp;Chemosphere\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e2021\u003c/strong\u003e,\u003cem\u003e\u0026nbsp;270\u003c/em\u003e,129441.\u003cbr\u003ehttps://doi.org/10.1016/j.chemosphere.2020.129441.\u003c/li\u003e\n \u003cli\u003eHvidtfeldt,U.A.;S\u0026oslash;rensen,M.;Geels,C.;Ketzel,M.;Khan,J.;Tj\u0026oslash;nneland,A.;Overvad,K.;Brandt,J.;Raaschou-Nielsen,O.Long-termresidentialexposuretoPM2.5,PM10,blackcarbon,NO2,andozoneandmortalityinaDanishcohort.\u003cem\u003e\u0026nbsp;Environ. 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USA\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e2018\u003c/strong\u003e,\u003cem\u003e\u0026nbsp;115\u003c/em\u003e,9592\u0026ndash;9597.https://doi.org/10.1073/pnas.1803222115.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"fine particulate matter, ozone, composite pollution, health benefits, economic benefits","lastPublishedDoi":"10.21203/rs.3.rs-8807618/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8807618/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study analyzes the spatiotemporal evolution of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e pollution across 17 cities in Henan Province, China, from 2020 to 2024, assessing associated health and economic impacts. Results indicate that while PM\u003csub\u003e2.5\u003c/sub\u003e levels showed a general decline, O\u003csub\u003e3\u003c/sub\u003e concentrations exhibited a fluctuating upward trend, with more pronounced pollution in northern and central regions. March\u0026ndash;May and September\u0026ndash;October were identified as peak periods for composite pollution. In 2024, PM\u003csub\u003e2.5\u003c/sub\u003e-related premature deaths decreased by 10.6\u0026ndash;12.0% compared to 2020, while O\u003csub\u003e3\u003c/sub\u003e-related deaths increased by approximately 22.0%. Scenario projections suggest that achieving Class I standards of GB 3095\u0026thinsp;\u0026minus;\u0026thinsp;2012 by 2030 could reduce premature deaths attributable to PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e by 83.3% and 13.5%, respectively, with potential economic benefits of RMB 65.8\u0026nbsp;billion. The findings emphasize the need for coordinated pollution control to maximize health and economic benefits.\u003c/p\u003e","manuscriptTitle":"Assessment of the Health and Economic Benefits of PM 2.5 -O 3 Composite Pollution in Henan Province, China, 2020–2024","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 11:26:22","doi":"10.21203/rs.3.rs-8807618/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"155028154608841240327849118548990613128","date":"2026-04-17T03:32:09+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"30683640464195949165083119822703523918","date":"2026-03-24T02:28:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24784463792641897444314079872782417585","date":"2026-03-22T08:36:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149853251659241572295207870991355492625","date":"2026-02-16T06:35:24+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-12T02:31:28+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-12T02:07:11+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-11T18:52:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-10T13:25:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-02-10T12:23:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f4dd694-9686-4e06-8baf-bc7931979610","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62981024,"name":"Earth and environmental sciences/Environmental sciences"},{"id":62981025,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":62981026,"name":"Social science/Environmental studies"}],"tags":[],"updatedAt":"2026-02-17T11:26:22+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 11:26:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8807618","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8807618","identity":"rs-8807618","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}