Spatiotemporal distributions of ambient SO2 across China based on satellite retrievals and ground observations: Substantial decrease in human exposure during 2013-2016

Joint effects of green space and air pollutant exposure on preterm birth: evidence from a nationwide study in China

An association between green space exposure and preterm birth has been reported. However, evidence on the joint effects of air pollutant and green space exposure on preterm birth from nationwide research is limited in China. Based on a nationwide cohort, this study aims to explore the effect of green space exposure on preterm birth and analyze the joint effects of green space and air pollutant. Logistic regression models were developed to analyze the effects of green space exposure, and interaction effects were evaluated by adding interaction terms between green space and air pollutants. From 2013 to 2019, this study included 2,294,188 records of newborn births, of which 82,921 were preterm births. The results show that for buffer zones with 250 m, 500 m, 1000 m, and 1500 m, every 0.1 unit increase in NDVI exposure was associated with a decrease in the risk of preterm birth by 5.5% (95% CI: 4.6-6.4%), 5.8% (95% CI: 4.9-6.6%), 6.1% (95% CI: 5.3-7.0%), and 5.6% (95% CI: 4.7-6.5%), respectively. Under high-level exposure to air pollutants, high-level NDVI exposure was more strongly negatively correlated with preterm birth than low-level NDVI exposure. High-level green space exposure might mitigate the adverse effect of air pollutants on preterm birth by promoting physical activity, reducing stress, and adsorbing pollutants. Further investigation is needed to explore how green space and air pollution interact and affect preterm birth, in order to improve risk management and provide a reference for newborn health.

The Influence of Airborne Particulate Matter on the Risk of Gestational Diabetes Mellitus: A Large Retrospective Study in Chongqing, China

Emerging research findings suggest that airborne particulate matter might be a risk factor for gestational diabetes mellitus (GDM). However, the concentration-response relationships and the susceptible time windows for different types of particulate matter may vary. In this retrospective analysis, we employ a novel robust approach to assess the crucial time windows regarding the prevalence of GDM and to distinguish the susceptibility of three GDM subtypes to air pollution exposure. This study included 16,303 pregnant women who received routine antenatal care in 2018-2021 at the Maternal and Child Health Hospital in Chongqing, China. In total, 2482 women (15.2%) were diagnosed with GDM. We assessed the individual daily average exposure to air pollution, including PM2.5, PM10, O3, NO2, SO2, and CO based on the volunteers' addresses. We used high-accuracy gridded air pollution data generated by machine learning models to assess particulate matter per maternal exposure levels. We further analyzed the association of pre-pregnancy, early, and mid-pregnancy exposure to environmental pollutants using a generalized additive model (GAM) and distributed lag nonlinear models (DLNMs) to analyze the association between exposure at specific gestational weeks and the risk of GDM. We observed that, during the first trimester, per IQR increases for PM10 and PM2.5 exposure were associated with increased GDM risk (PM10: OR = 1.19, 95%CI: 1.07~1.33; PM2.5: OR = 1.32, 95%CI: 1.15~1.50) and isolated post-load hyperglycemia (GDM-IPH) risk (PM10: OR = 1.23, 95%CI: 1.09~1.39; PM2.5: OR = 1.38, 95%CI: 1.18~1.61). Second-trimester O3 exposure was positively correlated with the associated risk of GDM, while pre-pregnancy and first-trimester exposure was negatively associated with the risk of GDM-IPH. Exposure to SO2 in the second trimester was negatively associated with the risk of GDM-IPH. However, there were no observed associations between NO2 and CO exposure and the risk of GDM and its subgroups. Our results suggest that maternal exposure to particulate matter during early pregnancy and exposure to O3 in the second trimester might increase the risk of GDM, and GDM-IPH is the susceptible GDM subtype to airborne particulate matter exposure.

Third trimester as the susceptibility window for maternal PM2.5 exposure and preterm birth: A nationwide surveillance-based association study in China

Maternal PM_2.5 exposure has been identified as a potential risk factor for preterm birth, yet the inconsistent findings on the susceptible exposure windows may be partially due to the influence of gaseous pollutants. This study aims to examine the association between PM_2.5 exposure and preterm birth during different susceptible exposure windows after adjusting for exposure to gaseous pollutants. We collected 2,294,188 records of singleton live births from 30 provinces of China from 2013 to 2019, and the gridded daily concentrations of air pollutants (including PM_2.5, O₃, NO₂, SO₂, and CO) were derived by using machine learning models for assessing individual exposure. We employed logistic regression to develop single-pollutant models (including PM_2.5 only) and co-pollutant models (including PM_2.5 and a gaseous pollutant) to estimate the odds ratio for preterm birth and its subtypes, with adjustment for maternal age, neonatal sex, parity, meteorological conditions, and other potential confounders. In the single-pollutant models, PM_2.5 exposure in each trimester was significantly associated with preterm birth, and the third trimester exposure showed a stronger association with very preterm birth than that with moderate to late preterm birth. The co-pollutant models revealed that preterm birth might be significantly associated only with maternal exposure to PM_2.5 in the third trimester, and not with exposure in the first or second trimester. The observed significant associations between preterm birth and maternal PM_2.5 exposure in the first and second trimesters in single-pollutant models might primarily be influenced by exposure to gaseous pollutants. Our study provides evidence that the third trimester may be the susceptible window for maternal PM_2.5 exposure and preterm birth. The association between PM_2.5 exposure and preterm birth could be influenced by gaseous pollutants, which should be taken into consideration when evaluating the impact of PM_2.5 exposure on maternal and fetal health.

Trade-driven relocation of ground-level SO2 concentrations across Chinese provinces based on satellite observations

The influence of trade on ground-level SO2 concentrations in China was evaluated based on multiregional input-output (MRIO) analysis, using the ozone monitoring instrument (OMI) SO2 columns and SO2 profiles from an atmospheric chemical and transport model, MOZART-4. The provincial sum of ground-level SO2 concentrations has a good consistency with the provincial SO2 emissions (R = 0.65, p < 0.01). The provincial SO2 concentrations presented strong spatial variations, with a range of 5.1-50.6 μg/m3 and an average of 19.7 μg/m3 across China. The international trade increased the SO2 concentrations in all of the provinces and increased the national population-weighted SO2 (PWM-SO2) concentration by 2.9 μg/m3. Interprovincial trade within China decreased the ambient SO2 concentrations in Beijing, Tianjin, and Chongqing and the provinces in southeast and central China, but increased SO2 in the remaining provinces of China. In general, interprovincial trade decreased the national PWM-SO2 concentration by 5.3 μg/m3.

Spatiotemporal Regularity and Socioeconomic Drivers of the AQI in the Yangtze River Delta of China

Air pollution has caused adverse effects on the climate, the ecological environment and human health, and it has become a major challenge facing the world today. The Yangtze River Delta (YRD) is the region with the most developed economy and the most concentrated population in China. Identifying and quantifying the spatiotemporal characteristics and impact mechanism of air quality in this region would help in formulating effective mitigation policies. Using annual data on the air quality index (AQI) of 39 cities in the YRD from 2015 to 2018, the spatiotemporal regularity of the AQI is meticulously uncovered. Furthermore, a geographically weighted regression (GWR) model is used to qualify the geographical heterogeneity of the effect of different socioeconomic variables on the AQI level. The empirical results show that (1) the urban agglomeration in the YRD presents an air pollution pattern of being low in the northwest and high in the southeast. The spatial correlation of the distribution of the AQI level is verified. The spatiotemporal regularity of the “high clustering club” and the “low clustering club” is obvious. (2) Different socioeconomic factors show obvious geographically heterogeneous effects on the AQI level. Among them, the impact intensity of transportation infrastructure is the largest, and the impact intensity of the openness level is the smallest. (3) The upgrading of the industrial structure improves the air quality status in the northwest more than it does in the southeast. The impact of transportation infrastructure on the air pollution of cities in Zhejiang Province is significantly higher than the impact on the air pollution of other cities. The air quality improvement brought by technological innovation decreases from north to south. With the expansion of urban size, there is a law according to which air quality first deteriorates and then improves. Finally, the government should promote the upgrading of key industries, reasonably control the scale of new construction land, and increase the cultivation of local green innovative enterprises.

A data-augmentation approach to deriving long-term surface SO2 across Northern China: Implications for interpretable machine learning

Until recently, Northern China was one of the most SO₂ polluted regions in the world. The lack of long-term and spatially resolved surface SO₂ data hinders retrospective evaluation of relevant environmental policies and human health effects. This study aims to derive the spatiotemporal distribution of surface SO₂ across Northern China during 2005-2019. As "concept drift" causes substantial estimation bias in back-extrapolation, we propose a new approach named the robust back-extrapolation via data augmentation approach (RBE-DA) to model the long-term surface SO₂. The results show that the population-weighted regional SO₂ ([SO₂]_pw) increased from 2005 to 2007 and decreased steadily afterwards. The [SO₂]_pw decreased by 80.4% from 74.2 ± 28.4 μg/m³ in 2007 to 14.6 ± 4.8 μg/m³ in 2019. The predicted spatial distributions for each year show that the SO₂ pollution was severe (more than 20 μg/m³) in most areas of Northern China until 2017. By using model interpretation methods, we visually reveal the mechanism of estimation bias in the back-extrapolation. Specifically, the training data is severely imbalanced with respect to the satellite-retrieved SO₂ column densities (i.e., it is short on high-value samples), so the benchmark model is unable to extrapolate the effects of this important predictor. This study provides long-term surface SO₂ data for post hoc evaluation and human exposure assessment in Northern China, while demonstrating that the interpretable machine learning approach is critical for model diagnostics and refinement. Leveraging satellite retrievals, the RBE-DA approach can be applied worldwide to back-extrapolate various measures of air quality.

Simultaneous removal of NOX and SO2 from flue gas in an integrated FGD-CABR system by sulfur cycling-mediated Fe(II)EDTA regeneration

Chemical absorption-biological reduction (CABR) process is an attractive method for NO_X removal and Fe(II)EDTA regeneration is important to sustain high NO_X removal. In this study a sustainable and eco-friendly sulfur cycling-mediated Fe(II)EDTA regeneration method was incorporated in the integrated biological flue gas desulfurization (FGD)-CABR system. Here, we investigated the NO_X and SO₂ removal efficiency of the system under three different flue gas flows (100 mL/min, 500 mL/min, and 1000 mL/min) and evaluated the feasibility of chemical Fe(III)EDTA reduction by sulfide in series of batch tests. Our results showed that complete SO₂ removal was achieved at all the tested scenarios with sulfide, thiosulfate and S⁰ accumulation in the solution. Meanwhile, the total removal efficiency of NO_X achieved ∼100% in the system, of which 3.2%-23.3% was removed in spray scrubber and 76.7%-96.5% in EGSB reactor along with no N₂O emission. The optimal pH and S^2-/Fe(III)EDTA for Fe(II)EDTA regeneration and S⁰ recovery was 8.0 and 1:2. The microbial community analysis results showed that the cooperation of heterotrophic denitrifier (Saprospiraceae_uncultured and Dechloromonas) and iron-reducing bacteria (Klebsiella and Petrimonas) in EGSB reactor and sulfide-oxidizing, nitrate-reducing bacteria (Azoarcus and Pseudarcobacter) in spray scrubber contributed to the efficient removal of NO_X in flue gas.

Spatiotemporal variations of air pollutants based on ground observation and emission sources over 19 Chinese urban agglomerations during 2015-2019

The "comparative attitude" of urban agglomerations involves multidimensional perspectives such as infrastructure, ecological protection, and air pollution. Based on monitoring station data, comparative studies of multispatial, multitimescale and multiemission pollution sources of air quality on 19 urban agglomerations during the 13th Five-Year Plan period in China were explored by mathematical statistics. The comparison results are all visualized and show that clean air days gradually increased and occurred mainly in summer, especially in South and Southwest China. PM2.5, PM10 and O3 were still the main primary pollutants. PM2.5 is mainly concentrated in December, January and February, and PM10 is mainly concentrated in October-November and March-April. The O3 pollution in the Pearl River Delta and Beibu Gulf urban agglomerations located in the south is mainly concentrated from August to November, which is different from others from May to September. Second, from 2015 to 2019, the increasing rate of O3 concentration in any hour is higher than that of particulate matter (PM). Diurnal trends in O3 concentration in all directions also showed a single peak, with the largest increments that appeared between 13:00 and 16:00, while the spatial distribution of this peak was significantly regional, earlier in the east but later in the west. Third, this analysis indicated that the annual average air quality index (AQI) showed a gradually decreasing trend outward, taking the Central Plain urban agglomeration as the center. The ambient air pollutants are gradually moving southward and mainly concentrated in the Central Plains urban agglomeration from 2015 to 2019. Furthermore, in each urban agglomeration, the cumulative emission of PM2.5 is consisted of the four average emissions, which is approximately 2.5 times of that of PM10, and industries are the main sources of PM2.5, PM10 and VOCs (volatile organic compounds). VOCs and NOX increased in half of the urban agglomerations, which are the reasons for the increase in ozone pollution. The outcomes of this study will provide targeted insights on pollution prevention in urban agglomerations in the future.

Association between maternal exposure to gaseous pollutants and atrial septal defect in China: A nationwide population-based study

BACKGROUND

The association between maternal exposure to gaseous air pollutants and congenital heart defects (CHD) remains unclear. The concentration-response relationship and the time windows of susceptibility to gaseous pollutants may vary by pollutant species and CHD subtypes.

OBJECTIVE

We aimed to examine the relationship between maternal exposures to four species of gaseous pollutants (NO₂, O₃, SO₂, and CO) and atrial septal defect (ASD), which is a common subtype of CHD, and to determine the critical time windows of susceptibility for each gaseous pollutant.

METHODS

Among 1,253,633 infants born between October 1, 2013 and December 31, 2016 in China, 1937 newborns were diagnosed with isolated ASD, a prevalence of 1.55‰. Maternal exposures to the gaseous pollutants were estimated by matching the geocoded maternal addresses with the gridded ambient concentrations. The adjusted odds ratios (aOR) between exposures and ASD were quantified by using mixed-effects logistic regression models.

RESULTS

We found significantly positive associations between ASD and maternal exposures to NO₂, O₃, SO₂, and CO during entire pregnancy, first-, second-, and third-trimester. However, no statistically significant association was found between maternal exposure to PM_2.5, PM_2.5-10 and ASD risk (P > 0.05). In the fully adjusted model with respect to average exposure over entire pregnancy, the adjusted odds ratios (aOR) for each 10 μg/m³ increment of NO₂, O₃, SO₂ were 1.33 (95% CI: 1.22-1.45), 1.13 (95% CI: 1.10-1.16), 1.28 (95% CI: 1.20-1.35), respectively; the aOR for each 100 μg/m³ increment of CO was 1.10 (95% CI: 1.06-1.15). The observed concentration-response relationships varied by exposure periods and pollutants, with the strongest association for NO₂ during the 1st-8th embryology weeks, for O₃ during the third trimester, for SO₂ during the second trimester, and for CO without obvious variation.

CONCLUSIONS

The findings suggest an increased risk of ASD in association with maternal exposures to four common gaseous pollutants. From the perspective of birth defects prevention and ASD risk mitigation, it is critical to reduce maternal exposure to gaseous pollutants especially during the most susceptible time windows.

A robust approach to deriving long-term daily surface NO2 levels across China: Correction to substantial estimation bias in back-extrapolation

BACKGROUND

Long-term surface NO₂ data are essential for retrospective policy evaluation and chronic human exposure assessment. In the absence of NO₂ observations for Mainland China before 2013, training a model with 2013-2018 data to make predictions for 2005-2012 (back-extrapolation) could cause substantial estimation bias due to concept drift.

OBJECTIVE

This study aims to correct the estimation bias in order to reconstruct the spatiotemporal distribution of daily surface NO₂ levels across China during 2005-2018.

METHODS

On the basis of ground- and satellite-based data, we proposed the robust back-extrapolation with a random forest (RBE-RF) to simulate the surface NO₂ through intermediate modeling of the scaling factors. For comparison purposes, we also employed a random forest (Base-RF), as a representative of the commonly used approach, to directly model the surface NO₂ levels.

RESULTS

The validation against Taiwan's NO₂ observations during 2005-2012 showed that RBE-RF adequately corrected the substantial underestimation by Base-RF. The RMSE decreased from 10.1 to 8.2 µg/m³, 7.1 to 4.3 µg/m³, and 6.1 to 2.9 µg/m³ in predicting daily, monthly, and annual levels, respectively. For North China with the most severe pollution, the population-weighted NO₂ ([NO₂]_pw) during 2005-2012 was estimated as 40.2 and 50.9 µg/m³ by Base-RF and RBE-RF, respectively, i.e., 21.0% difference. While both models predicted that the national annual [NO₂]_pw increased during 2005-2011 and then decreased, the interannual trends were underestimated by >50.2% by Base-RF relative to RBE-RF. During 2005-2018, the nationwide population that lived in the areas with NO₂ > 40 µg/m³ were estimated as 259 and 460 million by Base-RF and RBE-RF, respectively.

CONCLUSION

With RBE-RF, we corrected the estimation bias in back-extrapolation and obtained a full-coverage dataset of daily surface NO₂ across China during 2005-2018, which is valuable for environmental management and epidemiological research.

Maternal exposure to sulfur dioxide before and after conception and the risk of hypospadias in offspring

As far as we know, there have been no studies exploring the association between maternal sulfur dioxide (SO₂ ) exposure and the risk of hypospadias in offspring. We aimed to evaluate this association during the 3 months before conception and the first trimester. A population-based case-control study was conducted in male infants, consisting of 348 cases of hypospadias and 4023 controls. Maternal exposure to SO₂ , particulate matter with aerodynamic diameters ≤10 μm (PM₁₀ ), and nitrogen dioxide (NO₂ ) was assessed by averaging the concentrations recorded at all stations in the mother's city of residence. Air pollutants were tested for multicollinearity using variance inflation factor analysis. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated by multivariable logistic regression models. SO₂ exposure during the 3 months before conception was significantly associated with the risk of hypospadias (highest tertile: OR = 7.40, 95% CI: 3.54-15.62). When focusing on shorter exposure windows, similar associations were observed for SO₂ exposure in the first and second month before and the first month after conception. In conclusion, maternal exposure to SO₂ during the 3 months before and the first and second months after conception may increase the risk of hypospadias in offspring.

Acute effects of short-term exposure to ambient air pollution on reproductive hormones in young males of the MARHCS study in China

Air pollution, which is associated with male reproductive health. However, it is unknown the acute effects of ambient air pollutants exposure on male reproductive hormones. The current study, we measured serum levels of reproductive hormone in 2030 blood samples gathered from The Male Reproductive Health in Chongqing College Students (MARHCS) cohort study. We derived a full coverage of ambient air pollutant (PM₁₀, PM_2.5, SO₂, NO₂, CO and O₃) concentrations by employing machine learning algorithms, and used a mixed-effect model to estimate single-day and cumulative effects of air pollutants exposure on serum reproductive hormones. Our results showed that (1) PM₁₀ and PM_2.5 concentrations were positively associated with estradiol (E₂) in both single and cumulative lag days, but were negatively associated with the ratio of Testosterone/E₂ (the T/E₂ ratio). NO₂ was positively associated with estradiol at lag day 2 (95% CI: 0.290, 0.881; corrected P = 0.048) and lag 0-2 days (95% CI: 0.523, 1.337; corrected P = 0.003), with progesterone (P) at lag day 2 and lag day 3 (corrected P < 0.05). There was also a positive association between CO exposure and progesterone at lag day 2. (2) SO₂ was inversely associated with E₂ at lag day 3, 4 and lag 0-4 days, and progesterone at lag day 0, 1, 2 and lag 0-1, 0-2, 0-4 days, but positively associated with the T/E₂ ratio at lag day 3, 4 and lag 0-4 days (corrected P < 0.05). O₃ exposure was negatively associated with E₂ at lag day 3 (95% CI: -0.216, -0.074, corrected P = 0.03). (3) No significant associations were found between the cumulative daily average air pollutant exposure of CO, O₃ and hormone outcomes. This study suggests that short-term exposure to air pollutants may thus alter reproductive hormone levels, especially on serum estradiol, progesterone levels and the T/E₂ ratio.

Association of exposure to ambient air pollution with ovarian reserve among women in Shanxi province of north China

Air pollution has been an important risk factor for female reproductive health. However, epidemiological evidence of ambient air pollution on the predictor for ovarian reserve (antral follicle count, AFC) is deficient. We aim to comprehensively evaluate the association of long-term exposure to ambient air pollution with AFC among women of reproductive age in Shanxi of north China. 600 women with spontaneous menstrual cycle, not using controlled ovarian stimulation, were enrolled in the retrospective study. Two distinct periods of antral follicle development were designed as exposure windows. Generalized linear model was employed to estimate the change of AFC associated with exposure of atmospheric pollutants (SO₂, NO₂, PM₁₀, PM_2.5, CO and O₃). Stratification analysis based on age (<30, ≥30 years), university degree (yes, no), years of exposure (2013-2016, 2017-2019) and duration of infertility (<2, 2-5, >5 years) along with two pollutants model were employed to further illustrate the association. We found every 10 μg/m³ increase in SO₂ concentration level during the entire development stage of antral follicle was associated with a -0.01 change in AFC (95% confidence interval: -0.016, -0.002) adjusting for the confounders including age, BMI, parity and infertility diagnosis factors. The significant association of increased SO₂ level with decreased AFC was particularly observed during the early transition from primary follicle to preantral follicle stage by 10 μg/m³ increase in SO₂ exposure level with a -0.01 change (95% CI: -0.015, -0.002) in AFC. The negative association was pronounced among women aged ≥30 years old, and also significant in two pollutants model after adjusting the confounders. No significant associations between other air pollutants and AFC were observed. Our finding suggests that long-term exposure to air pollutant SO₂ is associated with lower AFC, raising our concern that atmospheric SO₂ exposure may have potential adverse impact on women ovarian reserve.

Vertical distributions of tropospheric SO2 based on MAX-DOAS observations: Investigating the impacts of regional transport at different heights in the boundary layer

Information on the vertical distribution of air pollutants is essential for understanding their spatiotemporal evolution underlying urban atmospheric environment. This paper presents the SO₂ profiles based on ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) measurements from March 2018 to February 2019 in Hefei, East China. SO₂ decrease rapidly with increasing heights in the warm season, while lifted layers were observed in the cold season, indicating accumulation or long-range transport of SO₂ in different seasons might occur at different heights. The diurnal variations of SO₂ were roughly consistent for all four seasons, exhibiting the minimum at noon and higher values in the morning and late afternoon. Lifted layers of SO₂ were observed in the morning for fall and winter, implying the accumulation or transport of SO₂ in the morning mainly occurred at the top of the boundary layer. The bivariate polar plots showed that weighted SO₂ concentrations in the lower altitude were weakly dependent on wind, but in the middle and upper altitudes, higher weighted SO₂ concentrations were observed under conditions of middle-high wind speed. Concentration weighted trajectory (CWT) analysis suggested that potential sources of SO₂ in spring and summer were local and transported mainly occurred in the lower altitude from southern and eastern areas; while in fall and winter, SO₂ concentrations were deeply affected by long-range transport from northwestern and northern polluted regions in the middle and upper altitudes. Our findings provide new insight into the impacts of regional transport at different heights in the boundary layer on SO₂ pollution.

The implication of the air quality pattern in South Korea after the COVID-19 outbreak

By using multiple satellite measurements, the changes of the aerosol optical depth (AOD) and nitrogen dioxide (NO₂) over South Korea were investigated from January to March 2020 to evaluate the COVID-19 effect on the regional air quality. The NO₂ decrease in South Korea was found but not significant, which indicates the effects of spontaneous social distancing under the maintenance of ordinary life. The AODs in 2020 were normally high in January, but they became lower starting from February. Since the atmosphere over Eastern Asia was unusually stagnant in January and February 2020, the AOD decrease in February 2020 clearly reveals the positive effect of the COVID-19. Considering the insignificant NO₂ decrease in South Korea and the relatively long lifetime of aerosols, the AOD decrease in South Korea may be more attributed to the improvement of the air quality in neighboring countries. In March, regional atmosphere became well mixed and ventilated over South Korea, contributing to large enhancement of air quality. While the social activity was reduced after the COVID-19 outbreak, the regional meteorology should be also examined significantly to avoid the biased evaluation of the social impact on the change of the regional air quality.

Population based Air Pollution Exposure and its influence factors by Integrating Air Dispersion Modeling with GIS Spatial Analysis

Air pollution is a major environmental health problem. The study of interaction between air pollution and human will benefit to the human health and well-being of community. Both a model for assessing population relative risk of air pollution exposure (MAPRRAPE) and air pollution concentration methods were applied in a case study to determine the optimal method in evaluating risk of population exposure to Sulfur Dioxide (SO₂). The framework for building the MAPRRAPE was described in detail. Then, the spatial patterns of population by demographic characteristics exposed to SO₂ from industrial, vehicle, and the mixture of industrial and vehicle pollution sources, as well as an in-depth quantitative investigation using correlation analysis were studied for further source appointment. The results showed that the MAPRRAPE was more reliable than air pollution concentration model in determining population exposure risks by demographic characteristics. The high risk areas of whites exposed to SO₂ were larger than blacks and the other races due to a large number of whites, and other age groups exposed to SO₂ were larger than children and the old people. In addition, the correlation analyses showed that the relative risks of population by demographic characteristics exposed to SO₂ had a more significant correlation with vehicle pollution source than industrial pollution source. The results of source appointment thus demonstrated that vehicle pollution source was the main pollution source. This study suggests that there is a clear need for the implementation of programs and services that will reduce population exposed to air pollution with focusing on densely populated areas for an ultimate improvement of community health status and the environmental conditions.