Impact of diurnal variability and meteorological factors on the PM2.5 - AOD relationship: Implications for PM2.5 remote sensing

Estimating the spatiotemporal distribution of PM2.5 concentrations in Tianjin during the Chinese Spring Festival: Impact of fireworks ban

SETTING

off fireworks during the Spring Festival (SF) is a traditional practice in China. However, because of its environmental impact, the Chinese government has banned this practice completely. Existing evaluations of the effectiveness of firework prohibition policies (FPPs) lack spatiotemporal perspectives, making it difficult to comprehensively assess their effects on air quality. Consequently, this study used remote sensing technology based on aerosol optical depth and multiple variables, compared nine statistical learning methods, and selected the optimal model, transformer, to estimate daily spatiotemporal continuous PM2.5 concentration datasets for Tianjin from 2016 to 2020. The overall model accuracy reached a root mean square error of 15.30 μg/m³, a mean absolute error of 9.55 μg/m³, a mean absolute percentage error of 21.07%, and an R2 of 0.88. Subsequently, we analysed the variations in PM2.5 concentrations from three time dimensions-the entire year, winter, and SF periods-to exclude the impact of interannual variations on the experimental results. Additionally, we quantitatively estimated firework-specific PM2.5 concentrations based on time-series forecasting. The results showed that during the three years following the implementation of the FPPs, firework-specific PM2.5 concentrations decreased by 52.70%, 49.76%, and 86.90%, respectively, compared to the year before the implementation of the FPPs. Spatially, the central urban area and industrial zones are more affected by FPPs than the suburbs. However, daily variations of PM2.5 concentrations during the SF showed that high concentrations of PM2.5 produced in a short period will return to normal rapidly and will not cause lasting effects. Therefore, the management of fireworks needs to consider both environmental protection and the public's emotional attachment to traditional customs, rather than simply imposing a blanket ban on fireworks. We advocate improving firework policies in four aspects-production, sales, supervision, and control-to promote sustainable development of the ecological environment and human society.

Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations

In China, population growth and aging have partially negated the public health benefits of air pollution control measures, underscoring the ongoing need for precise PM2.5 monitoring and mapping. Despite its prevalence, the satellite-derived Aerosol Optical Depth (AOD) method for estimating PM2.5 concentrations often encounters significant spatial data gaps. Additionally, current research still needs better representation of PM2.5 spatiotemporal heterogeneity. Addressing these challenges, we developed a two-stage model employing the Extreme Gradient Boosting (XGBoost) algorithm. By incorporating improved spatiotemporal factors, we achieved high-precision and full-coverage daily 1-km PM2.5 mappings across China for the year 2020 without utilizing AOD products. Specifically, Model 1 develops improved temporal encodings and a terrain classification factor (DC), while Model 2 constructs an enhanced spatial autocorrelation term (Ps) by integrating observed and estimated values. Notably, Model 2 excelled in 10-fold sample-based cross-validation, achieving a coefficient of determination of 0.948, a mean absolute error of 3.792 μg/m³, a root mean square error of 7.144 μg/m³, and a mean relative error of 14.171%. Feature importance and Shapley Additive exPlanations (SHAP) analyses determined the relative importance of predictors in model training and outcome prediction, while correlation analysis identified strong links between improved temporal encodings, PM2.5 concentrations, and significant meteorological factors. Two-way Partial Dependence Plots (PDPs) further explored the interactions among these factors and their impact on PM2.5 levels. Compared to traditional methods, improved temporal encodings align more closely with seasonal variations and synergize more effectively with meteorological factors. Besides, the structured nature of DC aids in model training, while the improved Ps more effectively captures PM2.5's spatial autocorrelation, outperforming traditional Ps. Overall, this study effectively represents spatiotemporal information, thereby boosting model accuracy and enabling seamless large-scale PM2.5 estimations. It provides deep insights into variables and models, providing significant implications for future air pollution research.

Remote sensing estimation of regional PM2.5 based on GTWR model -A case study of southwest China

Air pollution in China has becoming increasingly serious in recent years with frequent incidents of smog. Parts of southwest China still experience high incidents of smog, with PM2.5 (particulate matter with diameter ≤2.5 μm) being the main contributor. Establishing the spatial distribution of PM2.5 in Southwest China is important for safeguarding regional human health, environmental quality, and economic development. This study used remote sensing (RS) and geographical information system (GIS) technologies and aerosol optical depth (AOD), digital elevation model (DEM), normalized difference vegetation index (NDVI), population density, and meteorological data from January to December 2018 for southwest China. PM2.5 concentrations were estimated using ordinary least squares regression (OLS), geographic weighted regression (GWR) and geographically and temporally weighted regression (GTWR). The results showed that: (1) Eight influencing factors showed different correlations to PM2.5 concentrations. However, the R2 values of the correlations all exceeded 0.3, indicating a moderate degree of correlation or more; (2) The correlation R2 values between the measured and remote sensed estimated PM2.5 data by OLS, GWR, and GTWR were 0.554, 0.713, and 0.801, respectively; (3) In general, the spatial distribution of PM2.5 in southwest of China decreases from the Northeast to Northwest, with moderate concentrations in the Southeast and Southwest; (4) The seasonal average PM2.5 concentration is high in winter, low in summer, and moderate in spring and autumn, whereas the monthly average shows a "V" -shaped oscillation change.

Multi-timescale variation characteristics of PM2.5 in different regions of China during 2014-2022

Over the past decade, China has achieved a significant reduction in PM2.5 concentrations. Due to the diversity of natural and artificial factors, regional differences are remarkable in the variation characteristics and have not been well addressed in previous studies. Based on hourly observed PM2.5 concentrations from 2014 to 2022, this study conducted a comprehensive analysis of variation characteristics on annual, seasonal, and diurnal scales, with a special focus on differences across major regions. Driving factors of the variations, the effectiveness of air pollution control efforts as well as future priorities were discussed. The annual PM2.5 concentrations in all regions showed an overall downward trend from 2014 to 2022, but the decline rates differed notably across the regions, with the maximum value nearly two times higher than the minimum value. The seasonal decline rates also differ from region to region, which could be partially attributed to the burning of crop residues and dust events. Northeast China was significantly affected by the burning of crop residues and experienced a big drop in the number of fire points in autumn, but a remarkable increase in spring. The spring dust events may greatly contribute to PM2.5 concentrations in northern and western China. For diurnal variation, nighttime concentrations were generally greater than daytime concentrations, and the nighttime concentrations were likely to increase in eastern regions and decrease in western regions. Furthermore, the daytime and nighttime ratios (calculated by daytime/nighttime concentration divided by the daily-mean concentration) exhibited different interannual trends, with the daytime ratios decreasing and nighttime ratios increasing, especially in the northeastern and western regions. The findings indicate that the air pollution control efforts have been generally successful, but with large regional disparities, and highlight the importance of controlling crop residue burning, dust events, and nighttime emissions for specific seasons and regions.

Prediction of air pollutant concentrations based on the long short-term memory neural network

In recent years, environmental problems caused by air pollutants have received increasing attention. Effective prediction of air pollutant concentrations is an important way to protect the public from harm. Recently, due to extreme climate and social development, the forest fire frequency has increased. During the biomass combustion process caused by forest fires, the content of particulate matter (PM) in the atmosphere increases significantly. However, most existing air pollutant concentration prediction methods do not consider the considerable impact of forest fires, and effective long-term prediction models have not been established to provide early warnings for harmful gases. Therefore, in this paper, we collected a daily air quality data set (aerodynamic diameter smaller than 2.5 µm, PM2.5) for Heilongjiang Province, China, from 2017 to 2023 and A novel Long Short-Term Memory (LSTM) model was proposed to effectively predict the situation of air pollutants. The model could automatically extract information of the effective time step from the historical data set and combine forest fire disturbance and climate data as auxiliary data to improve the model prediction ability. Moreover, we created artificial neural network (ANN) and permissive regression (support vector machine, SVR) models for comparative experiments. The results showed that the precision accuracy of the developed LSTM model is higher. Unlike the other models, the LSTM neural network model could effectively predict the concentration of air pollutants in long-term series. Regarding long-term observation missions (7 days), the proposed model performed well and stably, with R2 reaching over 88%.

Spatiotemporal empirical analysis of particulate matter PM2.5 pollution and air quality index (AQI) trends in Africa using MERRA-2 reanalysis datasets (1980-2021)

In this study, the spatial-temporal trends of PM2.5 pollution were analyzed for subregions in Africa and the entire continent from 1980 to 2021. The distributions and trends of PM2.5 were derived from the monthly concentrations of the aerosol species from MERRA-2 reanalysis datasets comprising of sulphates (SO4), organic carbon (OC), black carbon (BC), Dust2.5 and Sea Salt (SS2.5). The resulting PM2.5 trends were compared with the climate factors, socio-economic indicators, and terrain characteristics. Using the Mann-Kendall (M-K) test, the continent and its subregions showed positive trends in PM2.5 concentrations, except for western and central Africa which exhibited marginal negative trends. The M-K trends also determined Dust2.5 as the dominant contributing aerosol factor responsible for the high PM2.5 concentrations in the northern, western and central regions of Africa, while SO4 and OC were respectively the most significant contributors to PM2.5 in the eastern and southern Africa regions. For the climate factors, the PM2.5 trends were determined to be positively correlated with the wind speed trends, while precipitation and temperature trends exhibited low and sometimes negative correlations with PM2.5. Socio-economically, highly populated, and bare/sparse vegetated areas showed higher PM2.5 concentrations, while vegetated areas tended to have lower PM2.5 concentrations. Topographically, low laying regions were observed to retain the deposited PM2.5 especially in the northern and western regions of Africa. The Air Quality Index (AQI) results showed that 94 % of the continent had an average PM2.5 of 12-35 μg/m3 hence classified as "Moderate" AQI, and the rest of the continent's PM2.5 levels was between 35 and 55 μg/m3 implying AQI classification of "Unhealthy for Sensitive People". Northern and western Africa regions had the highest AQI, while southern Africa had the lowest AQI. The approach and findings in this study can be used to complement the evaluation and management of air quality in Africa.

When and where to exercise: An assessment of personal exposure to urban tropical ambient airborne pollutants in Singapore

BACKGROUND

Physical activity is associated with health benefits and has been shown to reduce mortality risk. However, exposure to high levels of ambient fine particulate matter (PM2.5) during exercise can potentially reduce the health benefits of physical activity. This study aims to assess and compare the PM2.5 concentrations of different exercise venues in Singapore by their location attributes and time of day.

METHODS

Personal PM2.5 exposures (μg/m3) at 24 common outdoor exercise venues in Singapore over 49 sampling days were collected using real-time personal sensors from September 2017 to January 2020. Wilcoxon rank-sum test and Kruskal-Wallis test were used to compare PM2.5 concentrations between different timings (peak (0700-0900; 1800-2000) vs. non-peak (0600-0700; 0900-1800; 2000-2300); weekend vs. weekday), and location attributes (near major roads (<50 m) vs. away from major roads (≥50 m)). Multivariable linear regression models were used to assess the associations between location attributes, timings and ambient PM2.5 with personal PM2.5 concentration, adjusting for potential confounders.

RESULTS

Compared with peak hours, exercising during non-peak hours was associated with a significantly lower PM2.5 exposure (median, 17.8 μg/m3 during peak vs. 14.5 μg/m3 during non-peak; P = 0.006). Exercise venues away from major roads have significantly lower PM2.5 concentrations as compared to those located next to major roads (median, 14.4 μg/m3 away from major roads vs. 18.5 μg/m3 next to major roads; P < 0.001). Individuals who exercised in parks experienced the highest PM2.5 exposure (median, 55.0 μg/m3) levels in the afternoon during 1400-1500. Furthermore, ambient PM2.5 concentration was significantly and positively associated with personal PM2.5 exposure (β = 0.85, P < 0.001).

CONCLUSIONS

Our findings suggest that exercising outdoors in the urban environment exposes individuals to differential levels of PM2.5 at different times of the day. Further research should investigate a wider variety of outdoor exercise venues, explore different types of air pollutants, and consider the varying activity patterns of individuals.

Estimation of health-related and economic impacts of PM2.5 in Arak, Iran, using BenMAP-CE

Ambient air quality is one of the most critical threats to human health. In this study, the health and economic benefits of reducing PM2.5 were estimated in the city of Arak during the period of 2017-2019. The concentration data were obtained from the Environmental Protection Organization of Central Province, while the demographic data were obtained from the website of the Iran Statistics Center. The number of premature deaths from all causes, ischemic heart disease, chronic obstructive pulmonary disease, and lung cancer, attributable to PM2.5 pollution was estimated using the Environmental Benefits Mapping and Analysis Program-Comprehensive Version (BenMAP_CE) to limit the guidelines of the World Health Organization. The results showed that improving air quality in 2017, 2018, and 2019 in Arak could prevent the deaths of 729, 654, and 460 people, respectively. The number of years of life lost (YLL) in 2017, 2018, and 2019 was 11383, 10362, and 7260 years, respectively. The total annual economic benefits of reducing the PM2.5 concentration in Arak under the proposed scenarios in 2017, 2018, and 2019 were estimated to be 309,225,507, 262,868,727, and 182,224,053 USD, respectively, using the statistical life method (VSL). Based on the results of this study, there are significant health and economic benefits to reducing PM2.5 concentrations in Arak City. Therefore, planning and adopting control policies to reduce air pollution in this city are necessary.

Wildland Fires Worsened Population Exposure to PM2.5 Pollution in the Contiguous United States

As wildland fires become more frequent and intense, fire smoke has significantly worsened the ambient air quality, posing greater health risks. To better understand the impact of wildfire smoke on air quality, we developed a modeling system to estimate daily PM2.5 concentrations attributed to both fire smoke and nonsmoke sources across the contiguous U.S. We found that wildfire smoke has the most significant impact on air quality in the West Coast, followed by the Southeastern U.S. Between 2007 and 2018, fire smoke contributed over 25% of daily PM2.5 concentrations at ∼40% of all regulatory air monitors in the EPA's air quality system (AQS) for more than one month per year. People residing outside the vicinity of an EPA AQS monitor (defined by a 5 km radius) were subject to 36% more smoke impact days compared with those residing nearby. Lowering the national ambient air quality standard (NAAQS) for annual mean PM2.5 concentrations to between 9 and 10 μg/m3 would result in approximately 35-49% of the AQS monitors falling in nonattainment areas, taking into account the impact of fire smoke. If fire smoke contribution is excluded, this percentage would be reduced by 6 and 9%, demonstrating the significant negative impact of wildland fires on air quality.

Influencing factors of PM2.5 concentration in the typical urban agglomerations in China based on wavelet perspective

PM2.5 is one of the most harmful air pollutants affecting sustainable economic and social development in China. The analysis of influencing factors affecting PM2.5 concentration is significant for the improvement of air quality. In this study, three typical urban agglomerations in China (Beijing‒Tianjin‒Hebei [BTH], the Yangtze River Delta [YRD], and the Pearl River Delta [PRD]) were studied using innovative trend analysis, a Bayesian statistical model, and partial wavelet and multiwavelet coherence to analyze PM2.5 concentration variations and multi-scale coupled oscillations between PM2.5 concentration and air pollutants/meteorological factors. The results showed that: (1) PM2.5 concentration time-series showed significant downward trends, which decreased as follows: BTH > YRD > PRD. The higher the pollution level, the greater the change trend. In BTH and the PRD, PM2.5 had obvious trends and seasonal change points; whereas, the PM2.5 time-series change point in the YRD was not obvious. (2) PM2.5 had significant intermittent resonance cycles with air pollutants and meteorological factors in different time domains. There were differences in the main controlling factors affecting PM2.5 among the three urban agglomerations. (3) The explanatory ability of air pollutant combinations for variations in PM2.5 was higher than that of meteorological factor combinations. However, the synergistic effect of air pollutants/meteorological factors could better explain the PM2.5 concentration variations on all time-frequency scales. The results of this study provide a reference for ecological improvement as well as collaborative governance of air pollution.

Impacts of haze and nitrogen oxide alleviation on summertime ozone formation: A modeling study over the Yangtze River Delta, China

The strict emission control measures have profoundly changed the air pollution in the Yangtze River Delta (YRD) region, China. However, the impacts of decreasing fine particulates (PM2.5) and nitrogen oxide (NOx) on summer ozone (O3) formation still remain disputable. We perform simulations in the 2018 summer over the YRD using the WRF-Chem model that considers the aerosol radiative forcing (ARF) and HO2 heterogeneous loss on aerosol surface. The model reasonably reproduces the measured spatiotemporal surface O3 and PM2.5 concentrations and aerosol compositions. Model sensitivity experiments show that the NOx mitigation during recent years changes daytime O3 formation in summer from the transition regime to the NOx-sensitive regime in the YRD. The decreasing NOx emission generally weakens O3 formation and lowers ambient O3 levels in summer during recent years, except for some urban centers of megacities. While, the haze alleviation characterized by a decline in ambient PM2.5 concentration in the past years largely counteracts the daytime O3 decrease caused by NOx mitigation, largely contributing to the persistently high levels of summertime O3. The counteracting effect is dominantly attributed to the attenuated ARF and minorly contributed by the suppressed HO2 uptake and heterogeneous loss on aerosol surface. These results highlight that the repeated O3 pollution in the YRD is closely associated with NOx and haze alleviation and more efforts must be taken to achieve lower O3 levels.

Assessing vulnerability of densely populated areas to air pollution using Sentinel-5P imageries: a case study of the Nile Delta, Egypt

Air pollution represents one of the major environmental stressors with serious implications on human health and ecosystem health. Recently remote sensing imageries; as an alternative cost and time-effective method compared with regular monitoring techniques, were used for provision of appropriate data concerning air quality over large areas. In this context, Sentinel-5P satellite provides high-resolution images of atmospheric pollutants including nitrogen dioxide, ozone, carbon monoxide (CO) and particulate matter (PM). The current work aims to delineate vulnerability of densely populated areas in Northern-Egypt to air pollution through retrieving CO and PM2.5 from Sentinel-5P images and validate the retrieved data through simultaneous In-Situ measurements. For this purpose, our approach comprised four-step methodology; data acquisition on study area, data manipulation, validation of retrieved air quality data and mapping the vulnerability to air pollution. Based on the data retrieved from the imagery, a composite vulnerability index for each CO and PM2.5 value was developed delineating the most vulnerable areas to air pollution in the Northern Nile Delta region. Such results revealed that Sentinel-5P imagery can serve as a valuable tool for monitoring air quality and assessing vulnerability of densely populated areas to air pollution. Accordingly, it can be concluded that the applied Sentinel-5P based model can be applied effectively for other air pollutants and can be extrapolated to other areas with similar and/or different environmental settings.

Air quality characteristics during 2016-2020 in Wuhan, China

Implementation of a clean air policy in China has high national importance. Here, we analyzed tempo-spatial characteristics of the concentrations of PM_2.5 (PM_2.5_C), PM₁₀ (PM₁₀_C), SO₂ (SO₂ _C), NO₂ (NO₂ _C), CO (CO _C), and maximum 8-h average O₃ (O₃_8h_C), monitored at 22 stations throughout the mega-city of Wuhan from January 2016 to December 2020, and their correlations with the meteorological and socio-economic factors. PM_2.5_C, PM₁₀_C, SO₂ _C, NO₂ _C, and CO _C showed similar monthly and seasonal trends, with minimum value in summer and maximum value in winter. However, O_{3_}8h_C showed an opposite monthly and seasonal change pattern. In 2020, compared to the other years, the annual average PM_2.5_C, PM₁₀_C, SO₂ _C, NO₂ _C, and CO _C were lower. PM_2.5_C and PM₁₀_C were higher in urban and industrial sites and lower in the control site. The SO₂_C was higher in industrial sites. The NO₂_C was lower, and O_{3_}8h_C was higher in suburban sites, while CO showed no spatial differences in their concentrations. PM_2.5 _C, PM₁₀ _C, SO₂ _C, NO₂ _C, and CO _C had positive correlations with each other, while O_{3_}8h_C showed more complex correlations with the other pollutants. PM_2.5_C, PM₁₀_C, SO₂ _C, and CO _C presented a significantly negative association with temperature and precipitation, while O₃ was significantly positively associated with temperature and negatively associated with relative air humidity. There was no significant correlation between air pollutants and wind speed. Gross domestic product, population, number of automobiles, and energy consumption play an important role in the dynamics of air quality concentrations. These all provided important information for the decision and policy-makers to effectively control the air pollution in Wuhan.

Spatiotemporally continuous estimates of daily 1-km PM2.5 concentrations and their long-term exposure in China from 2000 to 2020

Monitoring long-term variations in fine particulate matter (PM_2.5) is essential for environmental management and epidemiological studies. While satellite-based statistical/machine-learning methods can be used for estimating high-resolution ground-level PM_2.5 concentration data, their applications have been hindered by limited accuracy in daily estimates during years without PM_2.5 measurements and massive missing values due to satellite retrieval data. To address these issues, we developed a new spatiotemporal high-resolution PM_2.5 hindcast modeling framework to generate the full-coverage, daily, 1-km PM_2.5 data for China for the period 2000-2020 with improved accuracy. Our modeling framework incorporated information on changes in observation variables between periods with and without monitoring data and filled gaps in PM_2.5 estimates induced by satellite data using imputed high-resolution aerosol data. Compared to previous hindcast studies, our method achieved superior overall cross-validation (CV) R² and root-mean-square error (RMSE) of 0.90 and 12.94 μg/m³ and significantly improved the model performance in years without PM_2.5 measurements, raising the leave-one-year-out CV R² [RMSE] to 0.83 [12.10 μg/m³] at a monthly scale (0.65 [23.29 μg/m³] at a daily scale). Our long-term PM_2.5 estimates show a sharp decline in PM_2.5 exposure in recent years, but the national exposure level in 2020 still exceeded the first annual interim target of the 2021 World Health Organization air quality guidelines. The proposed hindcast framework represents a new strategy to improve air quality hindcast modeling and can be applied to other regions with limited air quality monitoring periods. These high-quality estimates can support both long- and short-term scientific research and environmental management of PM_2.5 in China.

Scientometric and multidimensional contents analysis of PM2.5 concentration prediction

The foundation for the environmental department to take suitable measures and make a significant contribution towards improving air quality is the precise and dependable prediction of PM_2.5 concentration. It is essential to review the development process and hotspots of PM_2.5 concentration prediction studies over the past 20 years (2000-2021) comprehensively and quantitatively. This study used detailed bibliometric methods and CiteSpace software to visually analyze the PM_2.5 pollution level. The outcomes found that the prediction research phases of PM_2.5 can be broadly divided into three phases and enter the rapid growth phase after 2017. Five categories of keywords are clustered, and the forecasting data and forecasting methods are typical cluster representatives. Then, the construction and processing of PM_2.5 concentration prediction datasets, the prediction methods and technical processes, and the determination of the prediction spatial-temporal scales are the main content of the analysis. In the future, it is necessary to concentrate on multi-source data fusion for PM_2.5 concentration prediction at multiple spatial-temporal scales and focus on technology integration and innovative applications in forecasting models, especially the optimal use of deep machine learning methods to improve prediction accuracy and practical application conversion.

Regional urbanicity levels modify the association between ambient air pollution and prevalence of obesity: A nationwide cross-sectional survey

Ambient air pollution exposure may increase the risk of obesity, but the population susceptibility associated with urbanicity has been insufficiently investigated. Based on a nationwide representative cross-sectional survey on 44,544 adults, high-resolution night light satellite remote sensing products, and multi-source ambient air pollution inversion data, the present study evaluated the associations of fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) concentrations with the prevalence of obesity and abdominal obesity. We further calculated the associations in regions with different urbanicity levels characterized by both administrative classification of urban/rural regions and night light index (NLI). We found that 10 μg/m³ increments in PM_2.5 at 1-year moving average and in NO₂ at 5-year moving average were associated with increased prevalence of obesity [odds ratios (OR) = 1.16 (1.14, 1.19); 1.12 (1.09, 1.15), respectively] and abdominal obesity [OR = 1.08 (1.07, 1.10); 1.07 (1.05, 1.09), respectively]. People in rural regions experienced stronger adverse effects than those in urban regions. For instance, a 10 μg/m³ increment in PM_2.5 was associated with stronger odds of obesity in rural regions than in urban regions [OR = 1.27 (1.23, 1.31) vs 1.10 (1.05, 1.14), P for interaction <0.001]. In addition, lower NLI values were associated with constantly amplified associations of PM_2.5 and NO₂ with obesity and abdominal obesity (all P for interaction <0.001). In summary, people in less urbanized regions are more susceptible to the adverse effects of ambient air pollution on obesity, suggesting the significance of collaborative planning of urbanization development and air pollution control, especially in less urbanized regions.

Using satellite data on remote transportation of air pollutants for PM2.5 prediction in northern Taiwan

Accurate PM2.5 prediction is part of the fight against air pollution that helps governments to manage environmental policy. Satellite Remote sensing aerosol optical depth (AOD) processed by The Multi-Angle Implementation of Atmospheric Correlation (MAIAC) algorithm allows us to observe the transportation of remote pollutants between regions. The paper proposes a composite neural network model, the Remote Transported Pollutants (RTP) model, for such long-range pollutant transportation that predicts more accurate local PM2.5 concentrations given such satellite data. The proposed RTP model integrates several deep learning components and learns from the heterogeneous features of various domains. We also detected remote transportation pollution events (RTPEs) at two reference sites from the AOD data. Extensive experiments using real-world data show that the proposed RTP model outperforms the base model that does not account for RTPEs by 17%-30%, 23%-26% and 18%-22% and state-of-the-art models that account for RTPEs by 12%-22%, 12%-14%, and 10%-11% at +4h to +24h, +28h to +48 hours, and +52h to +72h hours respectively.

[Effect of modification of antihypertensive medications on the association of nitrogen dioxide long-term exposure and chronic kidney disease]

OBJECTIVE

To investigate the potential effect of modification of antihypertensive medications on the association of nitrogen dioxide (NO₂) long-term exposure and chronic kidney disease (CKD).

METHODS

Data of the national representative sample of adult population from the China National Survey of Chronic Kidney Disease (2007-2010) were included in the analyses, and exposure data of NO₂ were collected and matched. Generalized mixed-effects models were used to analyze the associations between NO₂ and CKD, stratified by the presence of hypertension and taking antihypertensive medications. The stratified exposure-response curves of NO₂ and CKD were fitted using the natural spine smoothing function. The modifying effects of antihypertensive medications on the association and the exposure-response curve of NO₂ and CKD were analyzed.

RESULTS

Data of 45 136 participants were included, with an average age of (49.5±15.3) years. The annual average exposure concentration of NO₂ was (7.2±6.4) μg/m³. Altogether 6 517 (14.4%) participants were taking antihypertensive medications, and 4 833 (10.7%) participants were identified as having CKD. After adjustment for potential confounders, in the hypertension population not using antihypertensive medications, long-term exposure to NO₂ was associated with a significant increase risk of CKD (OR: 1.38, 95%CI: 1.24-1.54, P < 0.001); while in the hypertension population using antihypertensive medications, no significant association between long-term exposure to NO₂ and CKD (OR: 0.96, 95%CI: 0.86-1.07, P=0.431) was observed. The exposure-response curve of NO₂ and CKD suggested that there was a non-linear trend in the association between NO₂ and CKD. The antihypertension medications showed significant modifying effects both on the association and the exposure-response curve of NO₂ and CKD (interaction P < 0.001).

CONCLUSION

The association between long-term exposure to NO₂ and CKD was modified by antihypertensive medications. Taking antihypertensive medications may mitigate the effect of long-term exposure to NO₂ on CKD.

Health and economic impacts of ambient air particulate matter (PM2.5) in Karaj city from 2012 to 2019 using BenMAP-CE

The present study aims to estimate the effects of PM_2.5 on the health and economy of Karaj city from 2012 to 2019. In this study, mortality attributed to long-term exposure to PM_2.5 and its spatial distribution in Karaj over the 2012-2019 period were estimated using the Global Exposure Mortality Model (GEMM) concentration-response function and BenMAP software. PM_2.5 hourly concentration data of air quality monitoring stations were used to estimate PM_2.5 for the whole city of Karaj. The economic effects of this pollutant were also assessed using the value of statistical life (VSL) method. The results showed that the annual average PM_2.5 concentration during the studied time increased and was higher than the air quality guideline levels recommended by the World Health Organization. Also, the annual number of deaths attributed to PM_2.5 in adults (older than 25 years) was estimated to be about 1200. The highest to lowest proportions of PM_2.5-related deaths were non-accidental mortality, ischemic heart attack, stroke, acute respiratory tract infection, chronic obstructive pulmonary disease (COPD), and lung cancer, in the order of their appearance. The results showed that the economic loss attributed to this pollutant was estimated at 380 to 504 million USD per year. Due to the effects of PM_2.5 on health and the economy in this city, we suggest conducting special planning to control and reduce the concentration of ambient air particulate matter by improving the public transportation system and updating industrial processes.

Three-Dimensional Landscape Pattern Characteristics of Land Function Zones and Their Influence on PM2.5 Based on LUR Model in the Central Urban Area of Nanchang City, China

China's rapid urbanization and industrialization process has triggered serious air pollution. As a main air pollutant, PM_2.5 is affected not only by meteorological conditions, but also by land use in urban area. The impacts of urban landscape on PM_2.5 become more complicated from a three-dimensional (3D) and land function zone point of view. Taking the urban area of Nanchang city, China, as a case and, on the basis of the identification of urban land function zones, this study firstly constructed a three-dimensional landscape index system to express the characteristics of 3D landscape pattern. Then, the land-use regression (LUR) model was applied to simulate PM_2.5 distribution with high precision, and a geographically weighted regression model was established. The results are as follows: (1) the constructed 3D landscape indices could reflect the 3D characteristics of urban landscape, and the overall 3D landscape indices of different urban land function zones were significantly different; (2) the effects of 3D landscape spatial pattern on PM_2.5 varied significantly with land function zone type; (3) the effects of 3D characteristics of landscapes on PM_2.5 in different land function zones are expressed in different ways and exhibit a significant spatial heterogeneity. This study provides a new idea for reducing air pollution by optimizing the urban landscape pattern.

Drivers of PM2.5 in the urban agglomeration on the northern slope of the Tianshan Mountains, China

Fine particulate matter (PM_2.5) is a major source of air pollution in China. Although there have been many studies of the drivers of PM_2.5 pollution in the megacities clustered in eastern China, the behavior of PM_2.5 in the northwestern urban agglomeration is not well understood. This study used near-surface observation data for 2015-2019 obtained from the national air environmental monitoring network to examine variation in PM_2.5 in the urban agglomeration on the northern slopes of the Tianshan Mountains (UANSTM). Two-factor interaction provided new insights into the dominant factors of PM_2.5 in the study region. The annual average PM_2.5 concentrations over the study period was 54.3 μg/m³, with an exceedance rate of 23.3%. Wavelet analysis showed two dominant cycles of 320-370 d and 150-200 d with high pollution events occurring in winter. The generalized additive model (GAM) contained linear functions of pressure, non-linear functions of SO₂, NO₂, relative humidity, sunshine duration and temperature. The two most primary variables, NO₂ and SO₂, represent 20.65% and 19.54% of the total deviance explained, respectively, while the meteorological factors account for 36.1% of the total deviance explained. In addition, the interaction between NO₂ and other factors had the strongest effect on PM_2.5. The deviance explained in the two factor interaction model (88.5%) was higher than that in the single factor model (78.4%). Our study emphasized that interaction between meteorological factors and pollutant emissions enhanced the impact on PM_2.5 compared with individual factors, which can provide a scientific basis for developing effective emission reduction strategies in UANSTM.

Effects of Meteorology Changes on Inter-Annual Variations of Aerosol Optical Depth and Surface PM2.5 in China—Implications for PM2.5 Remote Sensing

PM2.5 retrieval from satellite-observed aerosol optical depth (AOD) is still challenging due to the strong impact of meteorology. We investigate influences of meteorology changes on the inter-annual variations of AOD and surface PM2.5 in China between 2006 and 2017 using a nested 3D chemical transport model, GEOS-Chem, by fixing emissions at the 2006 level. We then identify major meteorological elements controlling the inter-annual variations of AOD and surface PM2.5 using multiple linear regression. We find larger influences of meteorology changes on trends of AOD than that of surface PM2.5. On the seasonal scale, meteorology changes are beneficial to AOD and surface PM2.5 reduction in spring (1–50%) but show an adverse effect on aerosol reduction in summer. In addition, major meteorological elements influencing variations of AOD and PM2.5 are similar between spring and fall. In winter, meteorology changes are favorable to AOD reduction (−0.007 yr−1, −1.2% yr−1; p < 0.05) but enhanced surface PM2.5 between 2006 and 2017. The difference in winter is mainly attributed to the stable boundary layer that isolates surface PM2.5 from aloft. The significant decrease in AOD over the years is related to the increase in meridional wind speed at 850 hPa in NCP (p < 0.05). The increase of surface PM2.5 in NCP in winter is possibly related to the increased temperature inversion and more stable stratification in the boundary layer. This suggests that previous estimates of wintertime surface PM2.5 using satellite measurements of AOD corrected by meteorological elements should be used with caution. Our findings provide potential meteorological elements that might improve the retrieval of surface PM2.5 from satellite-observed AOD on the seasonal scale.

Spatiotemporal PM2.5 estimations in China from 2015 to 2020 using an improved gradient boosting decision tree

Fine particulate matter (PM_2.5) with spatiotemporal continuity can provide important basis for the assessment of adverse effects on human health. In recent years, researchers have done a lot of work on the surface PM_2.5 simulation. However, due to the limitations of data and models, it is difficult to accurately evaluate the spatial and temporal PM_2.5 variations on a fine scale. In this study, we adopted the multi-angle implementation of atmospheric correction (MAIAC) aerosol products, and proposed a spatiotemporal model based on the gradient boosting decision tree (GBDT) algorithm to retrieve PM_2.5 concentration across China from 2015 to 2020 at 1-km resolution. Our model achieved excellent performance, with overall CV-R² of 0.92, and annual CV-R² of 0.90-0.93. In addition, the model can also be used for evaluation on different time scales. Compared with previous studies, the model developed in our study performed better and more stable, which showed the highest accuracies in PM_2.5 estimation works at 1-km resolution. During the study period, the overall national PM_2.5 pollution showed a downward trend, with the annual mean concentration dropping from 42.42 μg/m³ to 27.91 μg/m³. The largest decrease occurred in Beijing-Tianjin-Hebei (BTH), with a trend of -5.17 μg/m³/yr, while it remains the most polluted region. The area meeting the secondary national air quality standard (<35 μg/m³) increased from ∼34% to ∼79%. These results indicate that the atmospheric environment has improved significantly. Moreover, different regions have different time nodes for the start of the continuous standard-met day during the year, and the duration is different as well. Overall, this study can provide reliable large-scale PM_2.5 estimations.

PM2.5 Exposure and Health Risk Assessment Using Remote Sensing Data and GIS

Assessing personal exposure risk from PM2.5 air pollution poses challenges due to the limited availability of high spatial resolution data for PM2.5 and population density. This study introduced a seasonal spatial-temporal method of modeling PM2.5 distribution characteristics at a 1-km grid level based on remote sensing data and Geographic Information Systems (GIS). The high-accuracy population density data and the relative exposure risk model were used to assess the relationship between exposure to PM2.5 air pollution and public health. The results indicated that the spatial-temporal PM2.5 concentration could be simulated by MODIS images and GIS method and could provide high spatial resolution data sources for exposure risk assessment. PM2.5 air pollution risks were most serious in spring and winter, and high risks of environmental health hazards were mostly concentrated in densely populated areas in Shanghai-Hangzhou Bay, China. Policies to control the total population and pollution discharge need follow the principle of adaptation to local conditions in high-risk areas. Air quality maintenance and ecological maintenance should be carried out in low-risk areas to reduce exposure risk and improve environmental health.

Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia

With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.

High Spatiotemporal Resolution PM2.5 Concentration Estimation with Machine Learning Algorithm: A Case Study for Wildfire in California

As an aggregate of suspended particulate matter in the air, atmospheric aerosols can affect the regional climate. With the help of satellite remote sensing technology to retrieve AOD (aerosol optical depth) on a global or regional scale, accurate estimation of PM2.5 concentration has become an important task to quantify the spatiotemporal distribution of AOD and PM2.5. However, due to the limitations of satellite platforms, sensors, and inversion algorithms, the spatiotemporal resolution of current major AOD products is still relatively low. Meanwhile, for the impact of cloud, the AOD products often have a serious data gap problem, which also objectively limits the spatiotemporal coverage of predicted PM2.5 concentration. Therefore, how to effectively improve the spatiotemporal resolution and coverage of PM2.5 concentration under the requisite accuracy is still a grand challenge. In this study, the fused high spatial-temporal resolution AOD data in our previous study were used to estimate the ground PM2.5 concentration through machine learning algorithms, the deep belief network (DBN). The PM2.5 data had spatiotemporal autocorrelation in geostatistics and followed the Gaussian kernel distribution. Hence, the autocorrelation model modified by Gaussian kernel function integrated with DBN algorithm, named Geoi-DBN, was used to estimate PM2.5 concentration. The cross-validation results showed that the Geoi-DBN (R2 = 0.86, RMSE = 6.84 µg m−3) performed better than the original DBN (R2 = 0.67, RMSE = 10.46 µg m−3). The final high quality PM2.5 concentration data can be applied for urban air quality monitoring and related PM2.5 exposure risk assessment such as wildfire.

Estimating High-Resolution PM2.5 Concentrations by Fusing Satellite AOD and Smartphone Photographs Using a Convolutional Neural Network and Ensemble Learning

Aerosol optical depth (AOD) data derived from satellite products have been widely used to estimate fine particulate matter (PM2.5) concentrations. However, existing approaches to estimate PM2.5 concentrations are invariably limited by the availability of AOD data, which can be missing over large areas due to satellite measurements being obstructed by, for example, clouds, snow cover or high concentrations of air pollution. In this study, we addressed this shortcoming by developing a novel method for determining PM2.5 concentrations with high spatial coverage by integrating AOD-based estimations and smartphone photograph-based estimations. We first developed a multiple-input fuzzy neural network (MIFNN) model to measure PM2.5 concentrations from smartphone photographs. We then designed an ensemble learning model (AutoELM) to determine PM2.5 concentrations based on the Collection-6 Multi-Angle Implementation of Atmospheric Correction AOD product. The R2 values of the MIFNN model and AutoELM model are 0.85 and 0.80, respectively, which are superior to those of other state-of-the-art models. Subsequently, we used crowdsourced smartphone photographs obtained from social media to validate the transferability of the MIFNN model, which we then applied to generate smartphone photograph-based estimates of PM2.5 concentrations. These estimates were fused with AOD-based estimates to generate a new PM2.5 distribution product with broader coverage than existing products, equating to an average increase of 12% in map coverage of PM2.5 concentrations, which grows to an impressive 25% increase in map coverage in densely populated areas. Our findings indicate that the robust estimation accuracy of the ensemble learning model is due to its detection of nonlinear correlations and high-order interactions. Furthermore, our findings demonstrate that the synergy of smartphone photograph-based estimations and AOD-based estimations generates significantly greater spatial coverage of PM2.5 distribution than AOD-based estimations alone, especially in densely populated areas where more smartphone photographs are available.

Combining Himawari-8 AOD and deep forest model to obtain city-level distribution of PM2.5 in China

PM_2.5 (fine particulate matter with aerodynamics diameter <2.5 μm) is the most important component of air pollutants, and has a significant impact on the atmospheric environment and human health. Using satellite remote sensing aerosol optical depth (AOD) to explore the hourly ground PM_2.5 distribution is very helpful for PM_2.5 pollution control. In this study, Himawari-8 AOD, meteorological factors, geographic information, and a new deep forest model were used to construct an AOD-PM_2.5 estimation model in China. Hourly cross-validation results indicated that estimated PM_2.5 values were consistent with the site observation values, with an R² range of 0.82-0.91 and root mean square error (RMSE) of 8.79-14.72 μg/m³, among which the model performance reached the optimum value between 13:00 and 15:00 Beijing time (R² > 0.9). Analysis of the correlation coefficient between important features and PM_2.5 showed that the model performance was related to AOD and affected by meteorological factors, particularly the boundary layer height. Deep forest can detect diurnal variations in pollutant concentrations, which were higher in the morning, peaked at 10:00-11:00, and then began to decline. High-resolution PM_2.5 concentrations derived from the deep forest model revealed that some cities in China are seriously polluted, such as Xi 'an, Wuhan, and Chengdu. Our model can also capture the direction of PM_2.5, which conforms to the wind field. The results indicated that due to the combined effect of wind and mountains, some areas in China experience PM_2.5 pollution accumulation during spring and winter. We need to be vigilant because these areas with high PM_2.5 concentrations typically occur near cities.

Obtaining vertical distribution of PM2.5 from CALIOP data and machine learning algorithms

Aerosol optical depth (AOD) has been widely used to estimate the near-surface PM_2.5 (fine particulate matter with particle size less than 2.5 μm). However, the total-column AOD obtained by passive remote sensing instruments can neither distinguish the contribution of AOD in various altitude layers nor obtain the vertical PM_2.5 concentration. In this study, we compared several AOD-PM_2.5 models including Extra Trees (ET), Random Forest (RF), Deep Neural Network (DNN), and Gradient Boosting Regression Tree (GBRT), and analyzed the corresponding results using AOD of different altitudes and auxiliary data from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The results indicate that the ET model performs best in terms of the model effectiveness and feature interpretation on the training dataset. We conclude that the feature importance of the bottom layer AOD is higher than that of the upper and total column AOD. The results showed that regional differences existed in the optimal height of the AOD-PM_2.5 correlation in study area. The results of cross-validation indicate that ET manages the most appealing overall performance with an R² (RMSE) of 0.85 (17.77 μg/m³). Regarding the 729 sites involved in this study, 73% had R² > 0.7, and the region or season with higher AOD feature importance achieves better model performance. The results of the AOD-PM_2.5 model in each layer were corrected using the AOD weight, to obtained the PM_2.5 vertical concentrations from 2015 to 2019. The results highlight that the high PM_2.5 concentration area is primarily near the ground and decreases with height. Additionally, the PM_2.5 vertical concentration in Beijing-Tianjin-Hebei (-1.80 μg/m³, P < 0.001), Central China (-1.62 μg/m³, P < 0.001), and Pearl River Delta (-0.66 μg/m³, P < 0.001) show an apparent downward trend. We believe that the vertical distribution analysis of PM_2.5 can provide meaningful information for studying air pollution.

A full-coverage estimation of PM2.5 concentrations using a hybrid XGBoost-WD model and WRF-simulated meteorological fields in the Yangtze River Delta Urban Agglomeration, China

In spite of the state-of-the-art performances of machine learning in the PM_2.5 estimation, the high-value PM_2.5 underestimation and non-random aerosol optical depth (AOD) missing are still huge obstacles. By incorporating wavelet decomposition (WD) into the extreme gradient boosting (XGBoost), a hybrid XGBoost-WD model was established to obtain the full-coverage PM_2.5 estimation at 3-km spatial resolution in the Yangtze River Delta Urban Agglomeration (YRDUA). In this study, 3-km-resolution meteorological fields simulated by WRF along with AOD derived from Moderate Resolution Imaging Spectroradiometer (MODIS) were served as explanatory variables. Model MW and Model NW were developed using XGBoost-WD for the areas with and without AOD respectively to obtain a full-coverage PM_2.5 mapping in the YRDUA. The XGBoost-WD model showed good performances in estimating PM_2.5 with R² of 0.80 in the Model MW and 0.87 in the Model NW. Moreover, the K-value of Model MW increased from 0.77 to 0.79 and that of Model NM increased from 0.81 to 0.86 compared with the model without the step of WD, indicating an improvement on the problem of PM_2.5 underestimation. Due to a better ability of capturing abrupt changes in the PM_2.5 concentrations, the spatial evolution of PM_2.5 during a typical pollution event could be mapped more accurately. Finally, the analysis of variable importance showed that the three most important variables in the estimation of the low-frequency coefficients of PM_2.5 (PM_2.5_A4) were temperature at 2 m (T2), day of year (DOY) and longitude (LON), while that in the high-frequency coefficients of PM_2.5 (PM_2.5_D) were CO, AOD and NO₂. This study not only provided an effective solution to the PM_2.5 underestimation and AOD missing problems in the PM_2.5 estimation, but also proposed a new method to further refine the sophisticated correlations between PM_2.5 and some spatiotemporal variables.

PM2.5 and PM10 during COVID-19 lockdown in Kuwait: Mixed effect of dust and meteorological covariates

This study investigated the impact of COVID-19 lockdown on particulate matter concentrations, specifically PM2.5 and PM10, in Kuwait. We studied the variations in PM2.5 and PM10 between the lockdown in 2020 with the corresponding periods of the years 2017-2019, and also investigated the differences in PM variations between the 'curfew' and 'non curfew' hours. We applied mixed-effect regression to investigate the factors that dictate PM variability (i.e., dust and meteorological covariates), and also processed satellite-based aerosol optical depths (AOD) to determine the spatial variability in aerosol loads. The results showed low PM2.5 concentration during the lockdown (33 μg/m3) compared to the corresponding previous three years (2017-2019); however, the PM10 concentration (122.5 μg/m3) increased relative to 2017 (116.6 μg/m3), and 2019 (92.8 μg/m3). After removing the 'dust effects', both PM2.5 and PM10 levels dropped by 18% and 31%, respectively. The mixed-effect regression model showed that high temperature and high wind speed were the main contributors to high PM2.5 and PM10, respectively, in addition to the dust haze and blowing dust. This study highlights that the reductions of anthropogenic source emissions are overwhelmed by dust events and adverse meteorology in arid regions, and that the lockdown did not reduce the high concentrations of PM in Kuwait.

Full-coverage spatiotemporal mapping of ambient PM2.5 and PM10 over China from Sentinel-5P and assimilated datasets: Considering the precursors and chemical compositions

Ambient concentrations of particulate matters (PM_2.5 and PM₁₀) are significant indicators for monitoring the air quality relevant to living conditions. At present, most remote sensing based approaches for the estimation of PM_2.5 and PM₁₀ employed Aerosol Optical Depth (AOD) products as the main variate. Nevertheless, the coverage of missing data is generally large in AOD products, which can cause deviations in practical applications of estimated PM_2.5 and PM₁₀ (e.g., air quality monitoring and exposure evaluation). To efficiently address this issue, our study explores a novel approach using the datasets of the precursors & chemical compositions for PM_2.5 and PM₁₀ instead of AOD products. Specifically, the daily full-coverage ambient concentrations of PM_2.5 and PM₁₀ are estimated at 5-km (0.05°) spatial girds across China based on Sentinel-5P and assimilated datasets (GEOS-FP). The estimation models are acquired via an advanced ensemble learning method named Light Gradient Boosting Machine in this paper. For comparison, the Deep Blue AOD product from VIIRS is adopted in a similar framework as a baseline (AOD-based). Validation results show that the ambient concentrations are well estimated through the proposed approach, with the space-based Cross-Validation R²s and RMSEs of 0.88 (0.83) and 11.549 (22.9) μg/m³ for PM_2.5 (PM₁₀), respectively. Meanwhile, the proposed approach achieves better performance than the AOD-based in different cases (e.g., overall and seasonal). Compared to the related previous works over China, the estimation accuracy of our method is also satisfactory. Regarding the mapping, the estimated results through the proposed approach display consecutive spatial distribution and can exactly express the seasonal variations of PM_2.5 and PM₁₀. The proposed approach could efficiently present daily full-coverage results at 5-km spatial grids. It has a large potential to be extended for providing global accurate ambient concentrations of PM_2.5 and PM₁₀ at multiple temporal scales (e.g., daily and annual).

Urbanization, ambient air pollution, and prevalence of chronic kidney disease: A nationwide cross-sectional study

An increasing number of studies have linked ambient air pollution to chronic kidney disease (CKD) prevalence. However, its potential effect modification by urbanization has not been investigated. Based on data of 47,204 adults from the China National Survey of Chronic Kidney Disease (CKSCKD) dataset, night light satellite remote sensing data and high-resolution air pollution inversion products, the present cross-sectional study investigated the association between fine particulate matter <2.5 mm in diameter (PM_2.5), nitrogen dioxide (NO₂), night light index (NLI) and CKD prevalence in China, and the effect modification by urbanization characterized by administrative classification and NLI on the pollutant-health associations. Our results showed that a 10-μg/m³ increase in PM_2.5 at 3-year moving average, a 10-μg/m³ increase in NO₂ at 5-year moving average, and a 10-U increase in NLI at 5-year moving average were significantly associated with increased odds of CKD prevalence [OR = 1.24 (95 %CI:1.14, 1.35); OR = 1.12 (95 %CI:1.09, 1.15); OR = 1.05 (95 %CI:1.02, 1.07)]. Meanwhile, the pollutant-health associations were more apparent in medium-urbanized areas compared to low- and high-urbanized areas. For instance, a 10-μg/m³ increase in PM_2.5 concentration at 2-year moving average was associated with increased odds of CKD in the areas with NLI level in the second [OR = 2.78 (95 %CI:1.77, 4.36)] and third quartiles [OR = 1.49 (95 %CI:1.14, 1.95)], compared to the lowest [OR = 0.96 (95% CI: 0.73, 1.26)] and highest [OR = 0.63 (95% CI: 0.39-1.02)] quartiles. PM_2.5 and NO₂ were associated with increased odds of CKD prevalence, especially in areas with medium NLI levels, suggesting the necessity of strengthening environmental management in medium-urbanized regions.

Prediction of PM2.5 Concentration Based on the LSTM-TSLightGBM Variable Weight Combination Model

PM2.5 is one of the main pollutants that cause air pollution, and high concentrations of PM2.5 seriously threaten human health. Therefore, an accurate prediction of PM2.5 concentration has great practical significance for air quality detection, air pollution restoration, and human health. This paper uses the historical air quality concentration data and meteorological data of the Beijing Olympic Sports Center as the research object. This paper establishes a long short-term memory (LSTM) model with a time window size of 12, establishes a T-shape light gradient boosting machine (TSLightGBM) model that uses all information in the time window as the next period of prediction input, and establishes a LSTM-TSLightGBM model pair based on an optimal weighted combination method. PM2.5 hourly concentration is predicted. The prediction results on the test set show that the mean squared error (MAE), root mean squared error (RMSE), and symmetric mean absolute percentage error (SMAPE) of the LSTM-TSLightGBM model are 11.873, 22.516, and 19.540%, respectively. Compared with LSTM, TSLightGBM, the recurrent neural network (RNN), and other models, the LSTM-TSLightGBM model has a lower MAE, RMSE, and SMAPE, and higher prediction accuracy for PM2.5 and better goodness-of-fit.

Evaluating the influence of land use and land cover change on fine particulate matter

Fine particulate matter (i.e. particles with diameters smaller than 2.5 microns, PM_2.5) has become a critical environmental issue in China. Land use and land cover (LULC) is recognized as one of the most important influence factors, however very fewer investigations have focused on the impact of LULC on PM_2.5. The influences of different LULC types and different land use and land cover change (LULCC) types on PM_2.5 are discussed. A geographically weighted regression model is used for the general analysis, and a spatial analysis method based on the geographic information system is used for a detailed analysis. The results show that LULCC has a stable influence on PM_2.5 concentration. For different LULC types, construction lands have the highest PM_2.5 concentration and woodlands have the lowest. The order of PM_2.5 concentration for the different LULC types is: construction lands > unused lands > water > farmlands >grasslands > woodlands. For different LULCC types, when high-grade land types are converted to low-grade types, the PM_2.5 concentration decreases; otherwise, the PM_2.5 concentration increases. The result of this study can provide a decision basis for regional environmental protection and regional ecological security agencies.

An Adjustment Approach for Aerosol Optical Depth Inferred from CALIPSO

The verification and correction of CALIPSO aerosol products is key to understanding the atmospheric environment and climate change. However, CALIPSO often cannot detect the full profile of aerosol for the low instrument sensitivity near the surface. Thus, a correction scheme for the aerosol extinction coefficient (AECs) in the planetary boundary layer (PBL) is proposed to improve the quality of the CALIPSO-based aerosol optical depth (AOD) at 532 nm. This scheme assumed that the aerosol is vertically and uniformly distributed below the PBL, and that the AECs in the whole PBL are equal to those at the top of the PBL; then, the CALIPSO AOD was obtained by vertically integrating AECs throughout the whole atmosphere. Additionally, the CALIPSO AOD and corrected CALIPSO AOD were validated against seven ground-based sites across eastern China during 2007–2015. Our results show that the initial CALIPSO AOD obtained by cloud filtering was generally lower than that of the ground-based observations. After accounting for the AECs in the PBL, the adjustment method tended to improve the CALIPSO AOD data quality. The average R (slope) value from all sites was improved by 7% (46%). Further, the relative distance between the ground track of CALIPSO and the ground station exhibited an influence on the validation result of CALIPSO AOD. The retrieval precision of CALIPSO AOD worsened with the increase in water vapor in the atmosphere. Our findings indicate that our scheme significantly improves the accuracy of CALIPSO AOD, which will help to provide alternative AOD products in the presence of severe atmospheric pollution.

Using infrared geostationary remote sensing to determine particulate matter ground-level composition and concentration

Speciated ground-level aerosol concentrations are required to understand and mitigate health impacts from dust storms, wildfires, and other aerosol emissions. Globally, surface monitoring is limited due to cost and infrastructure demands. While remote sensing can help estimate respirable (i.e. ground level) concentrations, current observations are restricted by inadequate spatiotemporal resolution, uncertainty in aerosol type, particle size, and vertical profile. One key issue with current remote sensing datasets is that they are derived from reflectances observed by polar-orbiting imagers, which means that aerosol is only derived during the daytime and only once or twice per day. Newer quantification methods using geostationary infrared (IR) data have focussed on detecting the presence, or absence, of an event. The determination of aerosol composition or particle size using IR exclusively has received little attention. This manuscript summarizes four scientific papers, published as part of a larger study, and identifies requirements for (a) using infrared radiance observations to obtain continual (i.e. day and night) concentration estimates; (b) increasing temporal resolution by using geostationary satellites; (c) utilizing all infrared channels to maximize spectral differences due to compositional changes; and (d) applying a high-pass filter (brightness temperature differences) to identify compositional variability. Additionally, (e) a preliminary calibration methodology was tested against three severe air quality case study incidents, namely, a dust storm, smoke from prescribed burns, and an ozone smog incident, near Sydney in eastern Australia which highlighted the ability of the method to determine atmospheric stability, clouds, and particle size. Geostationary remote sensing provides near-continuous data at a temporal resolution comparable to monitoring equipment. The spatial resolution (~ 4 km2 at NADIR) is adequate for large sources but coarse for localized sources. The spectral sensitivity of aerosol is limited and appears to be dominated by humidity changes rather than concentration or compositional changes. Geostationary remote sensing can be used to determine the timing, duration, and spatial extent of an air quality event. Brightness temperature differences can assist in qualifying composition with an order of magnitude estimate of concentration.

Acute effects of particulate matter with different sizes on respiratory mortality in Shenzhen, China

There are relatively few studies that focus on the health effects of exposure to size-specific particles on respiratory mortality in China. We aimed to examine the association between different particle sizes and mortality from cause-specific respiratory diseases. We used a time series model with a quasi-Poisson link to investigate the relationship between different particle sizes and mortality from respiratory diseases, chronic obstructive pulmonary diseases (COPD), pneumonia, and asthma in Shenzhen during 2014-2017. A total of 3716 mortalities due to respiratory diseases were collected. Both PM₁ and PM_2.5 were associated with mortality of overall respiratory diseases, COPD, and pneumonia. An interquartile range (IQR) increase in PM₁ at lag03 was associated with a 12.21% (95% CI: 2.59%, 22.75%) increase in respiratory mortality, and each IQR increase in PM_2.5 at lag03 corresponded to a 12.09% (95% CI: 2.52%, 22.56%) increase in respiratory mortality. PM_1-2.5 was not associated with mortality from all-cause or cause-specific respiratory diseases. This study suggests that both PM₁ and PM_2.5 may increase the risk of mortality due to respiratory diseases in Shenzhen, China.

Determinant Powers of Socioeconomic Factors and Their Interactive Impacts on Particulate Matter Pollution in North China

Severe air pollution has significantly impacted climate and human health worldwide. In this study, global and local Moran’s I was used to examine the spatial autocorrelation of PM_2.5 pollution in North China from 2000–2017, using data obtained from Atmospheric Composition Analysis Group of Dalhousie University. The determinant powers and their interactive effects of socioeconomic factors on this pollutant are then quantified using a non-linear model, GeoDetector. Our experiments show that between 2000 and 2017, PM_2.5 pollution globally increased and exhibited a significant positive global and local autocorrelation. The greatest factor affecting PM_2.5 pollution was population density. Population density, road density, and urbanization showed a tendency to first increase and then decrease, while the number of industries and industrial output revealed a tendency to increase continuously. From a long-term perspective, the interactive effects of road density and industrial output, road density, and the number of industries were amongst the highest. These findings can be used to develop the effective policy to reduce PM_2.5 pollution, such as, due to the significant spatial autocorrelation between regions, the government should pay attention to the importance of regional joint management of PM_2.5 pollution.

An interpretable self-adaptive deep neural network for estimating daily spatially-continuous PM2.5 concentrations across China

Accurate estimation of daily spatially-continuous PM_2.5 (fine particulate matter) concentration is a prerequisite to address environmental public health issues, and satellite-based aerosol optical depth (AOD) products have been widely used to estimate PM_2.5 concentrations using statistical-based or machine learning-based models. However, statistical-based models oversimplify the AOD-PM_2.5 relationships, whereas complex machine learning technologies ignore the spatiotemporal heterogeneity of the predictors and demonstrate shortage in interpretation. Besides, large AOD data gaps resulting in PM_2.5 estimation biases have been seldom imputed in previous studies, especially at national scales. To fill the above research gaps, this study attempts to present a feasible methodology to estimate daily spatially-continuous PM_2.5 concentrations in China. The AOD data gaps across China were first imputed via a random forest (RF) model. Then, an interpretable self-adaptive deep neural network (SADNN) model, incorporating AOD, meteorological and other auxiliary predictors, was developed to estimate daily spatially-continuous PM_2.5 concentrations from 2017 to 2018. Five-fold sample (site)-based cross-validation results showed a high accuracy of the SADNN model, with coefficient of determination and root mean square error values equal to 0.86 (0.84) and 13.07 (14.30) μg/m³, respectively, outperforming the standard DNN and the RF model. Furthermore, the SADNN model identified the spatiotemporal patterns of predictor importance, and demonstrated that the boundary layer height, elevation and AOD were the most important predictors both spatially and temporally. And the predictor importance in the Qinghai-Tibet Plateau was different from that in the rest of China. These results enhance our understanding of AOD-PM_2.5 relationships and elucidate the estimated PM_2.5 datasets with complete coverage are applicable for related air pollution studies and epidemiological cohort studies. Moreover, considering the effective nonlinear model capability and interpretability, the SADNN model is beneficial for not only PM_2.5 estimation but also other earth data and scenarios.

Satellite-based ground PM2.5 estimation using a gradient boosting decision tree

Fine particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) is one of the major air pollutants risks to human health worldwide. Satellite-based aerosol optical depth (AOD) products are an effective metric for acquiring PM_2.5 information, featuring broad coverage and high resolution, which compensate for the sparse and uneven distribution of existing monitoring stations. In this study, a gradient boosting decision tree (GBDT) model for estimating ground PM_2.5 concentration directly from AOD products across China in 2017, integrating human activities and various natural variables was proposed. The GBDT model performed well in estimating temporal variability and spatial contrasts in daily PM_2.5 concentrations, with relatively high fitted model (10-fold cross-validation) coefficients of determination of 0.98 (0.81), low root mean square errors of 3.82 (11.57) μg/m³, and mean absolute error of 1.44 (7.45) μg/m³. Seasonal examinations revealed that summer had the cleanest air with the highest estimation accuracies, whereas winter had the most polluted air with the lowest estimation accuracies. The model successfully captured the PM_2.5 distribution pattern across China in 2017, showing high levels in southwest Xinjiang, the North China Plain, and the Sichuan Basin, especially in winter. Compared with other models, the GBDT model showed the highest performance in the estimation of PM_2.5 with a 3-km resolution. This algorithm can be adopted to improve the accuracy of PM_2.5 estimation with higher spatial resolution, especially in summer. In general, this study provided a potential method of improving the accuracy of satellite-based ground PM_2.5 estimation.

The comparison of AOD-based and non-AOD prediction models for daily PM2.5 estimation in Guangdong province, China with poor AOD coverage

The large amount of missing values has challenged the application of satellite-retrieved aerosol optical depth (AOD) in mapping surface PM_2.5 concentrations. In this study, we developed a non-AOD random forest model to estimate daily concentrations of PM_2.5 in Guangdong Province, China, where more than 80% of AOD data were missing. The predictive ability of the non-AOD model was compared with that of a AOD-based model. Daily ground-based measurements of PM_2.5 were obtained from 148 ground-monitoring sites in Guangdong with a 60 km rectangle buffer from January 2016 to December 2018. Daily MODIS MAIAC AOD were downloaded from NASA at a resolution of approximately 1 km. Two random forest models were developed to predict ground-level PM_2.5 with one included AOD as a predictor and the other one without AOD. The two random forest models were developed using the same dataset and their predictive abilities were compared. The results of 10-fold cross validation (CV) showed that the non-AOD and AOD-based random forest models yielded similar performance. The CV R² (RMSE) for the non-AOD model in 2016-2018 were 0.82 (8.4 μg/m³), 0.81 (9.5 μg/m³) and 0.78 (9.4 μg/m³), and those for AOD-based model were 0.83 (8.2 μg/m³), 0.82 (9.2 μg/m³) and 0.80 (9.0 μg/m³), respectively. Higher consistency of estimated PM_2.5 were observed in areas close to monitoring sites than those far away from sites, and in southern coastal than northern areas. As the non-AOD random forest model is not affected by AOD missingness, it can be used for epidemiological studies to estimate individual-level exposure to PM_2.5 at a high resolution without spatial or temporal gaps.

PM2.5 Estimation and Spatial-Temporal Pattern Analysis Based on the Modified Support Vector Regression Model and the 1 km Resolution MAIAC AOD in Hubei, China

The satellite-retrieved Aerosol Optical Depth (AOD) is widely used to estimate the concentrations and analyze the spatiotemporal pattern of Particulate Matter that is less than or equal to 2.5 microns (PM2.5), also providing a way for the related research of air pollution. Many studies generated PM2.5 concentration networks with resolutions of 3 km or 10 km. However, the relatively coarse resolution of the satellite AOD products make it difficult to determine the fine-scale characteristics of PM2.5 distributions that are important for urban air quality analysis. In addition, the composition and chemical properties of PM2.5 are relatively complex and might be affected by many factors, such as meteorological and land cover type factors. In this paper, an AOD product with a 1 km spatial resolution derived from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, the PM2.5 measurements from ground sites and the meteorological data as the auxiliary variable, are integrated into the Modified Support Vector Regression (MSVR) model that proposed in this paper to estimate the PM2.5 concentrations and analyze the spatiotemporal pattern of PM2.5. Considering the relatively small dataset and the somewhat complex relationship between the variables, we propose a Modified Support Vector Regression (MSVR) model that based on SVR to fit and estimate the PM2.5 concentrations in Hubei province of China. In this paper, we obtained Cross Correlation Coefficient (R²) of 0.74 for the regression of independent and dependent variables, and the conventional SVR model obtained R² of 0.60 as comparison. We think our MSVR model obtained relatively good performance in spite of many complex factors that might impact the accuracy. We then utilized the optimal MSVR model to perform the PM2.5 estimating, analyze their spatiotemporal patterns, and try to explain the possible reasons for these patterns. The results showed that the PM2.5 estimations retrieved from 1 km MAIAC AOD could reflect more detailed spatial distribution characteristics of PM2.5 and have higher accuracy than that from 3 km MODIS AOD. Therefore, the proposed MSVR model can be a better method for PM2.5 estimating, especially when the dataset is relatively small.

Long-Term Exposure to Ambient PM2.5, Sunlight, and Obesity: A Nationwide Study in China

BACKGROUND

Accumulated researches revealed that both fine particulate matter (PM_2.5) and sunlight exposure may be a risk factor for obesity, while researches regarding the potential effect modification by sunlight exposure on the relationship between PM_2.5 and obesity are limited. We aim to investigate whether the effect of PM_2.5 on obesity is affected by sunlight exposure among the general population in China.

METHODS

A sample of 47,204 adults in China was included. Obesity and abdominal obesity were assessed based on body mass index, waist circumference and waist-to-hip ratio, respectively. The five-year exposure to PM_2.5 and sunlight were accessed using the multi-source satellite products and a geochemical transport model. The relationship between PM_2.5, sunshine duration, and the obesity or abdominal obesity risk was evaluated using the general additive model.

RESULTS

The proportion of obesity and abdominal obesity was 12.6% and 26.8%, respectively. Levels of long-term PM_2.5 ranged from 13.2 to 72.1 μg/m³ with the mean of 46.6 μg/m³. Each 10 μg/m³ rise in PM_2.5 was related to a higher obesity risk [OR 1.12 (95% CI 1.09-1.14)] and abdominal obesity [OR 1.10 (95% CI 1.07-1.13)]. The association between PM_2.5 and obesity varied according to sunshine duration, with the highest ORs of 1.56 (95% CI 1.28-1.91) for obesity and 1.66 (95% CI 1.34-2.07) for abdominal obesity in the bottom quartile of sunlight exposure (3.21-5.34 hours/day).

CONCLUSION

Long-term PM_2.5 effect on obesity risk among the general Chinese population are influenced by sunlight exposure. More attention might be paid to reduce the adverse impacts of exposure to air pollution under short sunshine duration conditions.

Validation and Calibration of CAMS PM2.5 Forecasts Using In Situ PM2.5 Measurements in China and United States

An accurate forecast of fine particulate matter (PM2.5) concentration in the forthcoming days is crucial since it can be used as an early warning for the prevention of general public from hazardous PM2.5 pollution events. Though the European Copernicus Atmosphere Monitoring Service (CAMS) provides global PM2.5 forecasts up to the next 120 h at a 3 h time interval, the data accuracy of this product had not been well evaluated. By using hourly PM2.5 concentration data that were sampled in China and United States (US) between 2017 and 2018, the data accuracy and bias levels of CAMS PM2.5 concentration forecast over these two countries were examined. Ground-based validation results indicate a relatively low accuracy of raw PM2.5 forecasts given the presence of large and spatially varied modeling biases, especially in northwest China and the western United States. Specifically, the PM2.5 forecasts in China showed a mean correlation value ranging 0.31–0.45 (0.24–0.42 in US) and RMSE of 38–83 (8.30–16.76 in US) μg/m3, as the forecasting time horizons increased from 3 h to 120 h. Additionally, the data accuracy was found to not only decrease with the increase of forecasting time horizons but also exhibit an evident diurnal cycle. This implies the current CAMS forecasting model failed to resolve the local processes that modulate the diurnal variability of PM2.5. Moreover, the data accuracy varied between seasons, as accurate PM2.5 forecasts were more likely to be derived in the autumn in China, whereas these were more likely in spring in the US. To improve the data accuracy of the raw PM2.5 forecasts, a statistical bias correction model was then established using the random forest method to account for large modeling biases. The cross-validation results clearly demonstrated the effectiveness and benefits of the proposed bias correction model, as the diurnal varied and temporally increasing modeling biases were substantially reduced after the calibration. Overall, the calibrated CAMS PM2.5 forecasts could be used as a promising data source to prevent general public from severe PM2.5 pollution events given the improved data accuracy.

Changes and Predictions of Vertical Distributions of Global Light-Absorbing Aerosols Based on CALIPSO Observation

Knowledge of the vertical distribution of absorbing aerosols is crucial for radiative forcing assessment, and its quasi real-time prediction is one of the keys for the atmospheric correction of satellite remote sensing. In this study, we investigated the seasonal and interannual changes of the vertical distribution of global absorbing aerosols based on satellite measurement from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and proposed a neural network (NN) model to predict the vertical distribution of global absorbing aerosols. Gaussian fitting was proposed to derive the maximum fitted particle number concentration (MFNC), altitude corresponding to MFNC (MFA), and standard deviation (MFASD) for vertical distribution of dust and smoke aerosols. Results showed that higher MFA values of dust and smoke aerosols mainly occurred over deserts and tropical savannas, respectively. For dust aerosol, the MFA is mainly observed at 0.5 to 6 km above deserts, and low MFNC values occur in boreal spring and winter while high values in summer and autumn. The MFA of smoke is systematically lower than that of dust, ranging from 0.5 to 3.5 km over tropical rainforest and grassland. Moreover, we found that the MFA of global dust and smoke had decreased by 2.7 m yr−1 (statistical significance p = 0.02) and 1.7 m yr−1 (p = 0.02) over 2007–2016, respectively. The MFNC of global dust has increased by 0.63 cm−3 yr−1 (p = 0.05), whereas that of smoke has decreased by 0.12 cm−3 yr−1 (p = 0.05). In addition, the determination coefficient (R2) of the established prediction models for vertical distributions of absorbing aerosols were larger than 0.76 with root mean square error (RMSE) less than 1.42 cm−3, which should be helpful for the radiative forcing evaluation and atmospheric correction of satellite remote sensing.

Construction of a virtual PM2.5 observation network in China based on high-density surface meteorological observations using the Extreme Gradient Boosting model

With increasing public concerns on air pollution in China, there is a demand for long-term continuous PM_2.5 datasets. However, it was not until the end of 2012 that China established a national PM_2.5 observation network. Before that, satellite-retrieved aerosol optical depth (AOD) was frequently used as a primary predictor to estimate surface PM_2.5. Nevertheless, satellite-retrieved AOD often encounter incomplete daily coverage due to its sampling frequency and interferences from cloud, which greatly affect the representation of these AOD-based PM_2.5. Here, we constructed a virtual ground-based PM_2.5 observation network at 1180 meteorological sites across China using the Extreme Gradient Boosting (XGBoost) model with high-density meteorological observations as major predictors. Cross-validation of the XGBoost model showed strong robustness and high accuracy in its estimation of the daily (monthly) PM_2.5 across China in 2018, with R², root-mean-square error (RMSE) and mean absolute error values of 0.79 (0.92), 15.75 μg/m³ (6.75 μg/m³) and 9.89 μg/m³ (4.53 μg/m³), respectively. Meanwhile, we find that surface visibility plays the dominant role in terms of the relative importance of variables in the XGBoost model, accounting for 39.3% of the overall importance. We then use meteorological and PM_2.5 data in the year 2017 to assess the predictive capability of the model. Results showed that the XGBoost model is capable to accurately hindcast historical PM_2.5 at monthly (R² = 0.80, RMSE = 14.75 μg/m³), seasonal (R² = 0.86, RMSE = 12.28 μg/m³), and annual (R² = 0.81, RMSE = 10.10 μg/m³) mean levels. In general, the newly constructed virtual PM_2.5 observation network based on high-density surface meteorological observations using the Extreme Gradient Boosting model shows great potential in reconstructing historical PM_2.5 at ~1000 meteorological sites across China. It will be of benefit to filling gaps in AOD-based PM_2.5 data, as well as to other environmental studies including epidemiology.

The Urban–Rural Heterogeneity of Air Pollution in 35 Metropolitan Regions across China

Urbanization and air pollution are major anthropogenic impacts on Earth’s environment, weather, and climate. Each has been studied extensively, but their interactions have not. Urbanization leads to a dramatic variation in the spatial distribution of air pollution (fine particles) by altering surface properties and boundary-layer micrometeorology, but it remains unclear, especially between the centers and suburbs of metropolitan regions. Here, we investigated the spatial variation, or inhomogeneity, of air quality in urban and rural areas of 35 major metropolitan regions across China using four different long-term observational datasets from both ground-based and space-borne observations during the period 2001–2015. In general, air pollution in summer in urban areas is more serious than in rural areas. However, it is more homogeneously polluted, and also more severely polluted in winter than that in summer. Four factors are found to play roles in the spatial inhomogeneity of air pollution between urban and rural areas and their seasonal differences: (1) the urban–rural difference in emissions in summer is slightly larger than in winter; (2) urban structures have a more obvious association with the spatial distribution of aerosols in summer; (3) the wind speed, topography, and different reductions in the planetary boundary layer height from clean to polluted conditions have different effects on the density of pollutants in different seasons; and (4) relative humidity can play an important role in affecting the spatial inhomogeneity of air pollution despite the large uncertainties.

Boundary Layer Height as Estimated from Radar Wind Profilers in Four Cities in China: Relative Contributions from Aerosols and Surface Features

The turbulent mixing and dispersion of air pollutants is strongly dependent on the vertical structure of the wind, which constitutes one of the major challenges affecting the determination of boundary layer height (BLH). Here, an adaptive method is proposed to estimate BLH from measurements of radar wind profilers (RWPs) in Beijing (BJ), Nanjing (NJ), Chongqing (CQ), and Wulumuqi (WQ), China, during the summer of 2019. Validation against simultaneous BLH estimates from radiosondes (RSs) yielded a correlation coefficient of 0.66, indicating that the method can be used to derive BLH from RWPs. Diurnal variations of BLH and the ventilation coefficient (VC) at four sites were then examined. A distinct diurnal cycle of BLH was observed over all four cities; BLH gradually increased from sunset, reached a maximum in the afternoon, and then dropped sharply after sunset. The maximum hourly average BLH (1.426 ± 0.46 km) occurred in WQ, consistent with the maximum hourly mean VC larger than 5000 m2/s observed there. By comparison, the diurnal variation of VC was not strong, with values ranging between 2000 and 3000 m2/s, likely owing to the high-humidity environment. Furthermore, surface sensible heat flux, latent heat flux, and dry mass of particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) concentrations were found to somehow affect the vertical structure of wind and thermodynamic features, leading to a difference between RS and RWP BLH estimates. This indicates that the atmospheric environment can affect BLH estimates using RWP data. The BLH results from RWPs were better in some specific cases. These findings show great potential of RWP measurements in air quality research, and will provide key data references for policy-making toward emission reductions.

Satellite measurements of aerosol optical depth and carbon monoxide and comparison with ground data

Satellite data of aerosol optical depths (AODs) from the moderate resolution imaging spectroradiometer (MODIS) and carbon monoxide (CO) columns from the measurements of pollution in the troposphere (MOPITT) were collected for the study in Northern Thailand. Comparative analyses were conducted of MODIS (Terra and Aqua) AODs with ground particulate matter with diameter below 10 microns (PM₁₀) concentrations and MOPITT CO surface/total columns with ground CO concentrations for 2014-2017. Temporal variations in both the satellite and ground datasets were in good agreement. High levels of air pollutants were common during March-April. The annual analysis of both satellite and ground datasets revealed the highest levels of air pollutants in 2016 and the lowest levels in 2017. The AODs and PM₁₀ concentrations were at higher levels in the morning than in the afternoon. The comparison between satellite products showed that AODs correlated better with the CO total columns than the CO surface columns. The regression analysis presented better performance of Aqua AODs-PM₁₀ than Terra AODs-PM₁₀ with correlation coefficients (r) of 0.72-0.83 and 0.57-0.79, respectively. Ground CO concentrations correlated better with MOPITT CO surface columns (r = 0.65-0.73) than with CO total columns (r = 0.56-0.72). The r values of satellite and ground datasets were greatest when the analysis was restricted to November-March (dry weather periods with possible low mixing height (MH)). Overall, the results suggested that the relationships between satellite and ground data can be used to develop predictive models for ground PM₁₀ and CO in northern Thailand, particularly during air pollution episodes located where ground monitoring stations are limited.