Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

Spatial characterization of periodic behaviors of ground PM2.5 concentration across the Yangtze River Delta and the North China Plain during 2014-2024: A new insight on driving processes of regional air pollution

Fine particulate matter (PM2.5) has long been the major air pollutant across China, but its periodic behaviors are still not comprehensively understood at a regional scale. Spatial characterization of the periodic behaviors can sufficiently extract the spatiotemporal information of PM2.5 time series and provide additional insights to understand key drivers of regional air pollution. This study collected PM2.5 hourly concentrations at 168 sites across the Yangtze River Delta (YRD) and the North China Plain (NCP) during 2014-2024 and identified PM2.5 periodic behaviors based on a fast Fourier transform (FFT) algorithm with a pseudo F statistic extraction and harmonic regression analysis with a linear trend term. Spatial characterization of PM2.5 concentration periodicity was explored in long-term trend, daily and annual cycles. Results showed more local emissions and atmospheric upward dissipation in the NCP resulted in a stronger daily vibration of PM2.5 concentration rather than in the YRD. Greater annual amplitude in the NCP than in the YRD reflected the significantly-elevated emissions from coal combustion for domestic heat in cold season. A strongly negative correlation between annual amplitude of PM2.5 concentrations and spatial latitude of monitoring sites across the NCP was attributed to the decreasing washing-out effect of precipitation from southeast to northwest. PM2.5 concentration across both regions was experienced a long-term decreasing trend in 2014-2024 and the trend has slowed down after 2021. This study provided a new insight on driving PM2.5 concentrations across the NCP and the YRD of China and underlined the importance of periodic behaviors as complementing PM2.5 characterization.

Temporal dynamics and relationship between negative air ions and environmental factors in subtropical forests, China

Negative air ions (NAIs) in forest air contribute to air purification, improve respiratory system function, enhance immunity, reduce fatigue, and alleviate stress and anxiety. However, NAIs are easily influenced by meteorological factors, air quality factors, and radiation factors, and the mechanisms of these influences remain unclear. To investigate the dynamic changes of NAIs and their driving factors, this study focuses on the long-term monitoring of NAIs in the Yunzhong Mountain forest of the South Asian subtropical region in China. Using linear mixed models, structural equation models, and other ecological niche models, the study explores the characteristics of the dynamic changes in NAIs and their responses to meteorological factors, air quality factors, and radiation factors in the Yunzhong Mountain forest. The results showed that: (1) The seasonal variation of NAIs in the Yunzhong Mountain forest followed the pattern of summer > autumn > winter > spring. In terms of monthly dynamics, the highest concentrations of NAIs occurred in August. Regarding daily dynamics, the concentration of NAIs gradually increased from early morning, reaching its peak at 8:00 AM, after which it declined, reaching the lowest concentrations between 11:00 AM and 12:00 PM. (2) Correlation analysis showed that the concentrations of NAIs were significantly negatively correlated with NO2, air temperature (AT), PM1, PM2.5, and PM10, and significantly positively correlated with air humidity (AH). However, generalized linear mixed analysis indicated that PM2.5, O3, NO2, and air pressure (AP) had significant negative effects on the seasonal, monthly, and daily concentrations of NAIs, while air humidity and temperature exhibited significant positive effects. Moreover, air quality factors had a greater impact than meteorological factors on seasonal and monthly concentrations, whereas meteorological factors had a more pronounced influence on daily negative air ion concentrations. (3) The latent variable of air quality factors had a direct negative effect on the concentration of NAIs, with O3, PM1, and PM2.5 exerting significant indirect negative effects through the air quality factors. The latent variable of meteorological factors had a significant positive effect on the concentration of NAIs. Additionally, radiation factors did not have a direct significant effect on negative air ions, but radiation factors (ultraviolet radiation and net radiation) produced significant indirect positive effects on NAIs through meteorological factors and air quality. The concentration of NAIs in the Yunzhong Mountain forest is highest between 7:00 and 8:00 AM during July and August in the summer, making it the most suitable time for outdoor activities. Meteorological factors, air quality, and radiation factors all influence the NAIs in Yunzhong Mountain. Even in the absence of direct effects, these factors can have indirect impacts through other variables.

Chronic and infectious respiratory mortality and short-term exposures to four types of pollen taxa in older adults in Michigan, 2006-2017

INTRODUCTION

Levels of plant-based aeroallergens are rising as growing seasons lengthen and intensify with anthropogenic climate change. Increased exposure to pollens could increase risk for mortality from respiratory causes, particularly among older adults. We determined short-term, lag associations of four species classes of pollen (ragweed, deciduous trees, grass pollen and evergreen trees) with respiratory mortality (all cause, chronic and infectious related) in Michigan, USA.

METHODS

We obtained records for all Michigan deaths from 2006-2017 from the Michigan Department of Health and Human Services (MDHHS). Deaths from infectious and chronic respiratory-related causes were selected using International Classification of Diseases (ICD-10) codes. Pollen data were obtained from a prognostic model of daily pollen concentrations at 25 km resolution. Case-crossover models with distributed lag non-linear crossbases for pollen were used to estimate associations between lags of daily pollen concentrations with mortality and to explore effect modification by sex and racial groups.

RESULTS

127,163 deaths were included in the study. Cumulative daily high concentrations (90th percentile) of deciduous broadleaf, grass and ragweed were associated with all-cause respiratory mortality at early lags with e.g., a 1.81 times higher risk of all respiratory deaths at cumulative 7 day lag exposure to deciduous broadleaf pollen at the 90th percentile (95% confidence interval: 1.04, 3.15). Exposure to high concentrations of grass and ragweed pollens was associated with increased risk for death from chronic respiratory causes. No association was found for any pollen species with death from infectious respiratory causes though there was a positive but non-significant association of exposure to deciduous broadleaf and ragweed pollens. We found no evidence to suggest effect modification by race or sex.

CONCLUSIONS

Modelled exposures to high concentrations of pollen taxa were associated with increased all-cause and chronic respiratory mortality among older adults. Results suggest that pollen exposure may become more important to respiratory mortality as the temperatures increase and pollen seasons lengthen.

Predictive modeling of air quality in the Tehran megacity via deep learning techniques

Air pollution is a significant challenge in metropolitan areas, where increasing amounts of air pollutants threaten public health and environmental safety. The present study aims to forecast the concentrations of various air pollutants, including CO, O3, NO2, SO2, PM10, and PM2.5, from 2013 to 2023 in the Tehran megacity, Iran, via deep learning (DL) models and evaluate their effectiveness over conventional machine learning (ML) methods. Key driving variables, including temperature, relative humidity, dew point, wind speed, and air pressure, were considered. R-squared (R2), root-mean-square error (RMSE), mean absolute error (MAE), and mean-square error (MSE) were used to assess and compare the models. This research demonstrated that DL models typically outperform ML models in forecasting air pollution. Gated recurrent units (GRUs), fully connected neural networks (FCNNs), and convolutional neural networks (CNNs) recorded R2 and MSE values of 0.5971 and 42.11 for CO, 0.7873 and 171.40 for O3, and 0.4954 and 25.17 for SO2, respectively. Consequently, the FCNN and GRU presented remarkable performance in predicting NO2 (R2 = 0.6476 and MSE = 75.16), PM10 (R2 = 0.8712 and MSE = 45.11), and PM2.5 (R2 = 0.9276 and MSE = 58.12) concentrations. In terms of operational speed, the FCNN model exhibited the most efficiency, with a minimum and maximum runtime of 13 and 28 s, respectively. The feature importance analysis suggested that CO, O3 and NO2, SO2 and PM10, and PM2.5 are most affected by temperature, humidity, PM2.5, and PM10, respectively. Thus, temperature and humidity were the primary factors affecting the variability in pollutant concentrations. The conclusions confirm that the DL models achieve significant accuracy and serve as essential instruments for managing air pollution, providing practical insights for decision-makers to adopt efficient air quality control strategies.

Causality-Driven Feature Selection for Calibrating Low-Cost Airborne Particulate Sensors Using Machine Learning

With escalating global environmental challenges and worsening air quality, there is an urgent need for enhanced environmental monitoring capabilities. Low-cost sensor networks are emerging as a vital solution, enabling widespread and affordable deployment at fine spatial resolutions. In this context, machine learning for the calibration of low-cost sensors is particularly valuable. However, traditional machine learning models often lack interpretability and generalizability when applied to complex, dynamic environmental data. To address this, we propose a causal feature selection approach based on convergent cross mapping within the machine learning pipeline to build more robustly calibrated sensor networks. This approach is applied in the calibration of a low-cost optical particle counter OPC-N3, effectively reproducing the measurements of PM1 and PM2.5 as recorded by research-grade spectrometers. We evaluated the predictive performance and generalizability of these causally optimized models, observing improvements in both while reducing the number of input features, thus adhering to the Occam's razor principle. For the PM1 calibration model, the proposed feature selection reduced the mean squared error on the test set by 43.2% compared to the model with all input features, while the SHAP value-based selection only achieved a reduction of 29.6%. Similarly, for the PM2.5 model, the proposed feature selection led to a 33.2% reduction in the mean squared error, outperforming the 30.2% reduction achieved by the SHAP value-based selection. By integrating sensors with advanced machine learning techniques, this approach advances urban air quality monitoring, fostering a deeper scientific understanding of microenvironments. Beyond the current test cases, this feature selection method holds potential for broader applications in other environmental monitoring applications, contributing to the development of interpretable and robust environmental models.

Combination of ionizing radiation and 2-thio-6-azauridine induces cell death in radioresistant triple negative breast cancer cells by downregulating CD151 expression

BACKGROUND

Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer and is frequently resistant to therapy, ultimately resulting in treatment failure. Clinical trials have demonstrated the potential of sensitizing radiation therapy (RT)-resistant TNBC through the combination of chemotherapy and RT. This study sought to explore the potential of CD151 as a therapy response marker in the co-treatment strategy involving ionizing radiation (IR) and the repurposed antiviral drug 2-Thio-6-azauridine (TAU) for sensitizing RT-resistant TNBC (TNBC/RR).

METHODS

The investigation encompassed a variety of assessments, including viability using MTT and LDH assays, cell proliferation through BrdU incorporation and clonogenic assays, cell cycle analysis via flow cytometry, cell migration using wound scratch and Boyden chamber invasion assays, DNA damage assessment through γH2AX analysis, apoptosis evaluation through acridine-orange and ethidium bromide double staining assays, as well as caspase 3 activity measurement using a colorimetric assay. CD151 expression was examined through ELISA, flow cytometry and RT-qPCR.

RESULTS

The results showed a significant reduction in TNBC/RR cell viability following co-treatment. Moreover, the co-treatment reduced cell migration, induced apoptosis, downregulated CD151 expression, and increased caspase 3 activity in TNBC/RR cells. Additionally, CD151 was predicted to serve as a therapy response marker for co-treatment with TAU and IR.

CONCLUSION

These findings suggest the potential of combination treatment with IR and TAU as a promising strategy to overcome RT resistance in TNBC. Furthermore, CD151 emerges as a valuable therapy response marker for chemoradiotherapy.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57 µg/m3 and PM10 at 39.58 µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Parsimonious estimation of hourly surface ozone concentration across China during 2015-2020

Surface ozone is an important air pollutant detrimental to human health and vegetation productivity, particularly in China. However, high resolution surface ozone concentration data is still lacking, largely hindering accurate assessment of associated environmental impacts. Here, we collected hourly ground ozone observations (over 6 million records), remote sensing products, meteorological data, and social-economic information, and applied recurrent neural networks to map hourly surface ozone data (HrSOD) at a 0.1° × 0.1° resolution across China during 2015-2020. The coefficient of determination (R2) values in sample-based, site-based, and by-year cross-validations were 0.72, 0.65 and 0.71, respectively, with the root mean square error (RMSE) values being 11.71 ppb (mean = 30.89 ppb), 12.81 ppb (mean = 30.96 ppb) and 11.14 ppb (mean = 31.26 ppb). Moreover, it exhibits high spatiotemporal consistency with ground-level observations at different time scales (diurnal, seasonal, annual), and at various spatial levels (individual sites and regional scales). Meanwhile, the HrSOD provides critical information for fine-resolution assessment of surface ozone impacts on environmental and human benefits.

The impact of Chinese foreign direct investment and environmental tax revenues on air degradation in Europe: a spatial regression approach, 2000-2020

This study analyzes air pollution through the effects of China's FDI in 27 European countries over a 20-year period, with a focus on the impact of environmental tax revenues (ETRs) and the environmental context in China. The relationship is estimated through spatial regressions that account for the presence of air pollutants in neighboring countries. The findings suggest that China's FDI in Europe does not contribute to air pollution but rather has a positive impact. The presence of environmental charges filters out non-polluting investments, which has a non-linear relationship with PM2.5 pollution rates. The study also concludes that air pollution is closely linked to the global environmental context, highlighting the positive effects of international agreements in the fight against climate change. Specifically, the study finds a link between China's efforts to address its polluting activities and their impact on European air quality.

The effects of surface vegetation coverage on the spatial distribution of PM2.5 in the central area of Nanchang City, China

The frequent occurrence of haze has caused widespread concern in China, and PM2.5 is thought to be the main cause. Previous research showed that PM2.5 was not only influenced by meteorological conditions but also by land cover especially surface vegetation. It was concluded that PM2.5 concentration is significantly influenced by surface vegetation, but spatially how and in what manner are still unanswered. Taking the central area of Nanchang City, China, as a case, this study firstly applied land use regression (LUR) model to simulate the distribution of PM2.5 in 2020. Then, the dichotomous model was used to determine vegetation coverage. A statistical regression model was used to analyze the influence of vegetation cover on PM2.5 and the scale effects. The results showed that (1) vegetation coverage and PM2.5 concentration were both significantly negatively correlated at the spatial scales selected for this study. (2) The effect of vegetation coverage on PM2.5 varied with season and the 500 m had the best correlation. (3) The non-linear regression model fits better than the linear model, and the effect of vegetation coverage on PM2.5 was complex. (4) The effect of vegetation coverage on PM2.5 concentration was different with PM2.5 concentration level. The higher the PM2.5 concentration, the more pronounced the effect of vegetation coverage on it. This study proposed the idea and method of coupling vegetation coverage with PM2.5 concentration at the regional scale from gradient landscape's point of view and provided some references for mitigating PM2.5 pollution through optimizing urban vegetation patterns.

Detection of forest fires and pollutant plume dispersion using IoT air quality sensors

The widespread adoption of Internet of Things (IoT) sensors has revolutionized our understanding of the formation and mitigation of air pollution, significantly improving the accuracy of predictions related to air quality and emission sources. This study demonstrates the use of IoT air quality sensors to detect forest fire incidents by focusing on an area affected by forest fires in Tak Province, Thailand, from January to May 2021. We employed PM2.5 and carbon monoxide measurements from IoT sensors for forest fire detection and utilized the number of hotspots reported through satellite and human observations to identify forest fire incidents. Our data analysis revealed three distinct periods with forest fires and three periods without fires (non-forest fires). For model training, two forest fire and non-forest fire periods were selected and the remaining periods were set aside for validation. J48, a computer algorithm that helps make decisions by organizing information into a tree-like structure based on key characteristics, was used to construct the decision-tree model. Our model achieved an accuracy rate of 72% when classifying forest fire incidents using the training data and a solid accuracy of 69% on the validation data. In addition, we investigated the dispersion of PM2.5 plumes using a regression model. Notably, our findings highlighted the robust explanatory power of the lag time in PM2.5, for predicting PM2.5, in the next 15 min. Our analysis highlights the potential of IoT-based air quality sensors to enhance forest fire detection and predict pollution plume dispersion once fires are detected.

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.

Quantifying the scale-dependent relationships of PM2.5 and O3 on meteorological factors and their influencing factors in the Beijing-Tianjin-Hebei region and surrounding areas

To investigate the variations of PM2.5 and O3 and their synergistic effects with influencing factors at different time scales, we employed Bayesian estimator of abrupt seasonal and trend change to analyze the nonlinear variation process of PM2.5 and O3. Wavelet coherence and multiple wavelet coherence were utilized to quantify the coupling oscillation relationships of PM2.5 and O3 on single/multiple meteorological factors in the time-frequency domain. Furthermore, we combined this analysis with the partial wavelet coherence to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationships. The results obtained from this comprehensive analysis are as follows: (1) The seasonal component of PM2.5 exhibited a change point, which was most likely to occur in January 2017. The trend component showed a discontinuous decline and had a change point, which was most likely to appear in February 2017. The seasonal component of O3 did not exhibit a change point, while the trend component showed a discontinuous rise with two change points, which were most likely to occur in July 2018 and May 2017. (2) The phase and coherence relationships of PM2.5 and O3 on meteorological factors varied across different time scales. Stable phase relationships were observed on both small- and large-time scales, whereas no stable phase relationship was formed on medium scales. On all-time scales, sunshine duration was the best single variable for explaining PM2.5 variations and precipitation was the best single variable explaining O3 variations. When compared to single meteorological factors, the combination of multiple meteorological factors significantly improved the ability to explain variations in PM2.5 and O3 on small-time scales. (3) Atmospheric teleconnection factors were important driving factors affecting the response relationships of PM2.5 and O3 on meteorological factors and they had greater impact on the relationship at medium-time scales compared to small- and large-time scales.

Air pollution and motor vehicle collisions in New York city

Road traffic accidents are a pervasive feature of everyday life, killing 36,500 people, injuring 4.5 million and, overall, generating costs to the American society of $340 billion in 2019. Understanding the underlying factors can improve the design of prevention strategies. We use all road traffic collisions in New York City between 2013 and 2021 (N = 1,269,600) and match each individual collision to the nearest weather and air pollution station. Our study uses highly disaggregated data using an hourly frequency of collisions at a fine spatial level incorporating various air pollutants and weather factors. We employ an instrumental variable approach using temperature inversions to provide exogenous variation in air pollution addressing endogeneity and measurement error concerns. We find that higher concentrations of carbon monoxide (CO) and sulfur dioxide (SO2) increase the number of collisions but leave the severity (persons injured or killed) unaffected. Part of this can be explained by the effect of air pollutants on aggressive behavior: CO (p < .05) and SO2 (p < .01) increase the number of collisions caused by aggressive driving. Interestingly, this channel is only present in male drivers. Our results provide additional evidence that air pollution not only adversely affects health, but also has "non-health" related effects which are costly for the society.

Meteorological influences on co-occurrence of O3 and PM2.5 pollution and implication for emission reductions in Beijing-Tianjin-Hebei

Co-occurrence of surface ozone (O3) and fine particulate matter (PM2.5) pollution (CP) was frequently observed in Beijing-Tianjin-Hebei (BTH). More than 50% of CP days occurred during April-May in BTH, and the CP days reached up to 11 in two months of 2018. The PM2.5 or O3 concentration associated with CP was lower than but close to that in O3 and PM2.5 pollution, indicating compound harms during CP days with double-high concentrations of PM2.5 and O3. CP days were significantly facilitated by joint effects of the Rossby wave train that consisted of two centers associated with the Scandinavia pattern and one center over North China as well as a hot, wet, and stagnant environmental condition in BTH. After 2018, the number of CP days decreased sharply while the meteorological conditions did not change significantly. Therefore, changes in meteorological conditions did not really contribute to the decline of CP days in 2019 and 2020. This implies that the reduction of PM2.5 emission has resulted in a reduction of CP days (about 11 days in 2019 and 2020). The differences in atmospheric conditions revealed here were helpful to forecast the types of air pollution on a daily to weekly time scale. The reduction in PM2.5 emission was the main driving factor behind the absence of CP days in 2020, but the control of surface O3 must be stricter and deeper.

Electronic Supplementary Material

Supplementary material is available in the online version of this article at 10.1007/s11430-022-1070-y.

PM2.5: Epigenetic Alteration in Lung Physiology and Lung Cancer Pathogenesis

Particulate matter (PM) has a very negative impact on human health, specifically the respiratory system. PM comes in many forms, among these is PM2.5,which is a major risk factor for lung cancer and other cardiovascular diseases. PM is inherent in emissions from industrial production, manufacturing, vehicle exhaust, mining, and cigarette smoking. For this reason, the composition of PM differs from area to area although its primary constituents are heavy metals and petroleum elements. PM has a long and toxic impact on human health. After extended exposure to PM2.5 the mortality rate for lung cancer patients increases. Already, lung cancer is the leading cause of death globally with the highest mortality rate. PM2.5 creates epigenetic changes in miRNA, histone modification, and DNA methylation, causing tumorigenesis followed by lung cancer.

Impact of the ‘Coal-to-Natural Gas’ Policy on Criteria Air Pollutants in Northern China

During the last decades, China had issued a series of stringent control measures, resulting in a large decline in air pollutant concentrations. To quantify the net change in air pollutant concentrations driven by emissions, we developed an approach of determining the closed interval of the deweathered percentage change (DPC) in the concentration of air pollutants on an annual scale, as well as the closed intervals of cumulative DPC in a year compared with that in the base year. Thus, the hourly mean mass concentrations of criteria air pollutants to determine their interannual variations and the closed intervals of their DPCs during the heating seasons from 2013 to 2019 in Qingdao (a coastal megacity) were analyzed. The seasonal mean SO2 concentration decreased from 2013 to 2019. The seasonal mean CO, NO2, and PM2.5 concentrations also generally decreased from 2013 to 2017, but increased unexpectedly in 2018 (from 0.9 mg m−3 (CO), 42 µg m−3 (NO2), and 51 µg m−3 (PM2.5) in 2017 to 1.1 mg m−3, 48 µg m−3, and 64 µg m−3 in 2018, respectively). The closed intervals of DPC in concentrations of CO, NO2, and PM2.5 from the 2017 heating season (2017/2018) to the 2018 heating season (2018/2019) were obtained at (27%, 30%), (15%, 18%), and (30%, 33%), respectively. Such high positive endpoint values of the closed intervals, in contrast to their small interval lengths, indicate increased emissions of these pollutants and/or their precursors in 2018/2019 compared with 2017/2018, by minimizing the meteorological influences. The rebounds of CO, NO2, and PM2.5 in 2018/2019 were likely associated with a doubled increase in natural gas (NG) consumption implemented by the “coal-to-NG” project, as the total energy consumption showed little difference. Our results suggested an important role of the “coal-to-NG” project in driving concentrations of air pollutant increases in China in 2018/2019, which need integrated assessments.

A cohort study evaluating the risk of stroke associated with long-term exposure to ambient fine particulate matter in Taiwan

BACKGROUND

Evidences have shown that the stroke risk associated with long-term exposure to particulate matter with an aerodynamic diameter of ≤2.5 μm (PM_2.5) varies among people in North America, Europe and Asia, but studies in Asia rarely evaluated the association by stroke type. We examined whether long-term exposure to PM_2.5 is associated with developing all strokes, ischemic stroke and hemorrhagic stroke.

METHODS

The retrospective cohort study consisted of 1,362,284 adults identified from beneficiaries of a universal health insurance program in 2011. We obtained data on air pollutants and meteorological measurements from air quality monitoring stations across Taiwan in 2010-2015. Annual mean levels of all environmental measurements in residing areas were calculated and assigned to cohort members. We used Cox proportional hazards models to estimate hazard ratio (HR) and 95% confidence interval (CI) of developing stroke associated with 1-year mean levels of PM_2.5 at baseline in 2010, and yearly mean levels from 2010 to 2015 as the time-varying exposure, adjusting for age, sex, income and urbanization level.

RESULTS

During a median follow-up time of 6.0 years, 12,942 persons developed strokes, 9919 (76.6%) were ischemic. The adjusted HRs (95% CIs) per interquartile range increase in baseline 1-year mean PM_2.5 were 1.03 (1.00-1.06) for all stroke, 1.06 (1.02-1.09) for ischemic stroke, and 0.95 (0.89-1.10) for hemorrhagic stroke. The concentration-response curves estimated in the models with and without additional adjustments for other environmental measurements showed a positively linear association between baseline 1-year mean PM_2.5 and ischemic stroke at concentrations greater than 30 μg/m³, under which no evidence of association was observed. There was an indication of an inverse association between PM_2.5 and hemorrhagic stroke, but the association no longer existed after controlling for nitrogen dioxide or ozone. We found similar shape of the concentration-response association in the Cox regression models with time-varying PM_2.5 exposures.

CONCLUSION

Long-term exposure to PM_2.5 might be associated with increased risk of developing ischemic stroke. The association with high PM_2.5 concentrations remained significant after adjustment for other environmental factors.

Interpolation-Based Fusion of Sentinel-5P, SRTM, and Regulatory-Grade Ground Stations Data for Producing Spatially Continuous Maps of PM2.5 Concentrations Nationwide over Thailand

Atmospheric pollution has recently drawn significant attention due to its proven adverse effects on public health and the environment. This concern has been aggravated specifically in Southeast Asia due to increasing vehicular use, industrial activity, and agricultural burning practices. Consequently, elevated PM2.5 concentrations have become a matter of intervention for national authorities who have addressed the needs of monitoring air pollution by operating ground stations. However, their spatial coverage is limited and the installation and maintenance are costly. Therefore, alternative approaches are necessary at national and regional scales. In the current paper, we investigated interpolation models to fuse PM2.5 measurements from ground stations and satellite data in an attempt to produce spatially continuous maps of PM2.5 nationwide over Thailand. Four approaches are compared, namely the inverse distance weighted (IDW), ordinary kriging (OK), random forest (RF), and random forest combined with OK (RFK) leveraging on the NO2, SO2, CO, HCHO, AI, and O3 products from the Sentinel-5P satellite, regulatory-grade ground PM2.5 measurements, and topographic parameters. The results suggest that RFK is the most robust, especially when the pollution levels are moderate or extreme, achieving an RMSE value of 7.11 μg/m3 and an R2 value of 0.77 during a 10-day long period in February, and an RMSE of 10.77 μg/m3 and R2 and 0.91 during the entire month of March. The proposed approach can be adopted operationally and expanded by leveraging regulatory-grade stations, low-cost sensors, as well as upcoming satellite missions such as the GEMS and the Sentinel-5.