Spatiotemporal trends of PM2.5 concentrations in central China from 2003 to 2018 based on MAIAC-derived high-resolution data

Application of satellite remote sensing data and random forest approach to estimate ground-level PM2.5 concentration in Northern region of Thailand

Numerous epidemiological studies have shown that particulate matter with aerodynamic diameter up to 2.5 μm (PM2.5) is associated with many health consequences, where PM2.5 concentration obtained from the monitoring station was normally applied as the exposure level, so that the concentration of PM2.5 in unmonitored areas has not been captured. The satellite-derived aerosol optical depth (AOD) product is then used to spatially predict ground truth of PM2.5 concentration that covers the locations with no air quality monitoring station, but this method has seldom been developed in Thailand. This study aimed at estimating ground-level PM2.5 concentration at 3 km × 3 km spatial resolution over Northern region of Thailand in 2021 using the random forest model integrating the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products from Terra and Aqua satellites, meteorological factors, and land use data. A random forest model contained 100 decision trees was utilized to train the model, and 10-fold cross-validation approach was implemented to validate the model performance. The good consistency between actual (observed) and predicted concentrations of PM2.5 in Northern region of Thailand was observed, where a coefficient of determination (R2) and root mean square error (RMSE) of the model fitting were 0.803 and 14.30 μg/m3, respectively, and those of 10-fold cross-validation approach were 0.796 and 14.64 μg/m3, respectively. The three most important predictors for estimating the ground-level concentrations of PM2.5 in this study were normalized difference vegetation index (NDVI), relative humidity, and number of fire hotspot, respectively. Findings from this study revealed that integrating the MODIS AOD, meteorological variables, and land use data into the random forest model precisely and accurately estimated ground-level PM2.5 concentration over Northern region of Thailand that can be further used to investigate the effects of PM2.5 exposure on health consequences, even in unmonitored locations, in epidemiological studies.

Rural-Urban Disparities in Multimorbidity Associated With Climate Change and Air Pollution: A Longitudinal Analysis Among Chinese Adults Aged 45

Background and Objectives

Chronic conditions and multimorbidity are increasing worldwide. Yet, understanding the relationship between climate change, air pollution, and longitudinal changes in multimorbidity is limited. Here, we examined the effects of sociodemographic and environmental risk factors in multimorbidity among adults aged 45+ and compared the rural-urban disparities in multimorbidity.

Research Design and Methods

Data on the number of chronic conditions (up to 14), sociodemographic, and environmental factors were collected in 4 waves of the China Health and Retirement Longitudinal Study (2011-2018), linked with the full-coverage particulate matter 2.5 (PM2.5) concentration data set (2000-2018) and temperature records (2000-2018). Air pollution was assessed by the moving average of PM2.5 concentrations in 1, 2, 3, 4, and 5 years; temperature was measured by 1-, 2-, 3-, 4-, and 5-year moving average and their corresponding coefficients of variation. We used the growth curve modeling approach to examine the relationship between climate change, air pollution, and multimorbidity, and conducted a set of stratified analyses to study the rural-urban disparities in multimorbidity related to temperature and PM2.5 exposure.

Results

We found the higher PM2.5 concentrations and rising temperature were associated with higher multimorbidity, especially in the longer period. Stratified analyses further show the rural-urban disparity in multimorbidity: Rural respondents have a higher prevalence of multimorbidity related to rising temperature, whereas PM2.5-related multimorbidity is more severe among urban ones. We also found temperature is more harmful to multimorbidity than PM2.5 exposure, but PM2.5 exposure or temperature is not associated with the rate of multimorbidity increase with age.

Discussion and Implications

Our findings indicate that there is a significant relationship between climate change, air pollution, and multimorbidity, but this relationship is not equally distributed in the rural-urban settings in China. The findings highlight the importance of planning interventions and policies to deal with rising temperature and air pollution, especially for rural individuals.

Improved estimation of particulate matter in China based on multisource data fusion

Particulate matter (PM) is a global health concern and causes millions of premature deaths worldwide annually. High-resolution and full-coverage PM datasets are essential to support the accurate assessment of PM exposure. Here, a three-stage model framework is developed based on the Community Multiscale Air Quality (CMAQ) simulations (12 km) and multisource data fusion to estimate 1 km daily PM concentrations across China in 2015, including PM_2.5 (<2.5 μm) and PM₁₀ (<10 μm). The three-stage model performs well with cross-validation coefficient of determination (R²) of 0.91 and 0.87, and root mean square error (RMSE) of 17.3 μg/m³ and 27.2 μg/m³ for PM_2.5 and PM₁₀, respectively. After data fusion from multiple sources, the concentrations of PM_2.5 and PM₁₀ are in better agreement with ground observations compared to the CMAQ simulation with RMSE reduced by 72 % and 67 %. High PM_2.5 events mainly occur in the North China Plain, Yangtze River Delta, and Sichuan Basin, and PM₁₀ show similar spatial patterns to PM_2.5 in eastern China. These full-coverage PM datasets enable in-depth analysis of PM pollution over small areas and support future epidemiological studies and health assessments.

Estimating ground-level PM2.5 over Bangkok Metropolitan Region in Thailand using aerosol optical depth retrieved by MODIS

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM_2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM_2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM_2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM_2.5 was 22.37 (± 12.55) µg/m³ and the mean (standard deviation) of PM_2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m³, 33.60 (± 14.48) µg/m³, and 15.30 (± 4.78) µg/m³, respectively. The cross-validation yielded R ² of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM_2.5 concentration of 22.25 (± 9.97) µg/m³, 21.68 (± 9.14) µg/m³, 29.43 (± 9.45) µg/m³, and 15.74 (± 5.68) µg/m³ for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO₂ and O₃ into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM_2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM_2.5 concentration.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s11869-022-01238-4.

Full-Coverage PM2.5 Mapping and Variation Assessment during the Three-Year Blue-Sky Action Plan Based on a Daily Adaptive Modeling Approach

Owing to a series of air pollution prevention and control policies, China’s PM2.5 pollution has greatly improved; however, the long-term spatial contiguous products that facilitate the analysis of the distribution and variation of PM2.5 pollution are insufficient. Due to the limitations of missing values in aerosol optical depth (AOD) products, the reconstruction of full-coverage PM2.5 concentration remains challenging. In this study, we present a two-stage daily adaptive modeling framework, based on machine learning, to solve this problem. We built the annual models in the first stage, then daily models were constructed in the second stage based on the output of the annual models, which incorporated the parameter and feature adaptive tuning strategy. Within this study, PM2.5 concentrations were adaptively modeled and reconstructed daily based on the multi-angle implementation of atmospheric correction (MAIAC) AOD products and other ancillary data, such as meteorological factors, population, and elevation. Our model validation showed excellent performance with an overall R2 = 0.91 and RMSE = 9.91 μg/m3 for the daily models, along with the site-based cross-validation R2s and RMSEs of 0.86–0.87 and 12–12.33 μg/m3; these results indicated the reliability and feasibility of the proposed approach. The daily full-coverage PM2.5 concentrations at 1 km resolution across China during the Three-Year Blue-Sky Action Plan were reconstructed in this study. We analyzed the distribution and variations of reconstructed PM2.5 at three different time scales. Overall, national PM2.5 pollution has significantly improved with the annual average concentration dropping from 33.67–28.03 μg/m3, which demonstrated that air pollution control policies are effective and beneficial. However, some areas still have severe PM2.5 pollution problems that cannot be ignored. In conclusion, the approach proposed in this study can accurately present daily full-coverage PM2.5 concentrations and the research outcomes could provide a reference for subsequent air pollution prevention and control decision-making.

Random Forest Estimation and Trend Analysis of PM2.5 Concentration over the Huaihai Economic Zone, China (2000–2020)

Consisting of ten cities in four Chinese provinces, the Huaihai Economic Zone has suffered serious air pollution over the last two decades, particularly of fine particulate matter (PM2.5). In this study, we used multi-source data, namely MAIAC AOD (at a 1 km spatial resolution), meteorological, topographic, date, and location (latitude and longitude) data, to construct a regression model using random forest to estimate the daily PM2.5 concentration over the Huaihai Economic Zone from 2000 to 2020. It was found that the variable expressing time (date) had the greatest characteristic importance when estimating PM2.5. By averaging the modeled daily PM2.5 concentration, we produced a yearly PM2.5 concentration dataset, at a 1 km resolution, for the study area from 2000 to 2020. On comparing modeled daily PM2.5 with observational data, the coefficient of determination (R2) of the modeling was 0.85, the root means square error (RMSE) was 14.63 μg/m3, and the mean absolute error (MAE) was 10.03 μg/m3. The quality assessment of the synthesized yearly PM2.5 concentration dataset shows that R2 = 0.77, RMSE = 6.92 μg/m3, and MAE = 5.42 μg/m3. Despite different trends from 2000–2010 and from 2010–2020, the trend of PM2.5 concentration over the Huaihai Economic Zone during the 21 years was, overall, decreasing. The area of the significantly decreasing trend was small and mainly concentrated in the lake areas of the Zone. It is concluded that PM2.5 can be well-estimated from the MAIAC AOD dataset, when incorporating spatiotemporal variability using random forest, and that the resultant PM2.5 concentration data provide a basis for environmental monitoring over large geographic areas.

A unified empirical modeling approach for particulate matter and NO2 in a coastal city in China

Modeling air pollutants on a fine spatiotemporal scale is necessary for health studies that focus on critical short-term exposure windows. A unified empirical modeling approach is useful for health studies; however, it is unclear whether this approach can be used in a coastal city for air pollutants driven by local emissions and regional meteorological factors. An advanced empirical modeling approach was used to develop exposure models from October 2012 to December 2019, for particulate matter with aerodynamic diameters less than or equal to 2.5 and 10 μm (PM_2.5 and PM₁₀) and nitrogen dioxide (NO₂) in the coastal city of Shanghai, China. Air pollutant concentrations were obtained from daily measurements at 55 administrative monitoring sites that were integrated into three-day average concentrations. Data on a large array of geographic variables were collected, and their dimensions were reduced using the partial least squares regression method. A geostatistical model using the land-use regression approach in a universal kriging framework was developed to estimate short-term exposure concentrations. The prediction ability of the models were determined by leave-one (site)-out cross-validation (LOOCV) and external validation (EV). Compared to the LOOCV results, the EV results for PM_2.5 and PM₁₀ were consistently reliable, but the EV for NO₂ had a larger root mean squared error. The temporal random effects involved in the model structure were interpreted using sensitivity analyses. This affected the short-term PM_2.5 and PM₁₀ model predictions. This unified empirical modeling approach was successfully used for particulate matter in Shanghai, where air pollution is affected by complex regional and meteorological conditions. These exposure models are going to be applied for making exposure predictions at residential locations for short-term exposure predictions in the "Growth trajectories and air pollution" (GAAP) study in Shanghai that focuses on maternal and early life exposure to air pollutants.

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.

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.

What drives long-term PM2.5-attributable premature mortality change? A case study in central China using high-resolution satellite data from 2003 to 2018

Ambient PM_2.5 was reported to be related to numerous negative health outcomes, leading to adverse public health impacts in many countries such as China. Despite the apparent reduction in PM_2.5 levels over China due to its emission control policies in recent years, the health burdens were not reduced as much as expected. This calls for a comprehensive analysis to explain the reasons behind to provide a useful reference for formulating effective emission control strategies. Taking central China as an example due to its large population and high levels of PM_2.5, this study quantified the spatiotemporal dynamics of premature mortality associated with PM_2.5 pollution in central China for each year during 2003-2018 and applied a decomposition analysis to dissect the contribution of various driving factors including ambient PM_2.5 level, demographic distribution and baseline incidence rate of four diseases related to air pollution. Results show significant spatiotemporal variations in PM_2.5-attributed health impact in central China, including Henan, Hubei, and Hunan provinces. Five Henan cities had the largest PM_2.5-attributable premature mortality (∼8-12 K premature mortalities), while three Hubei cities and one Hebei city had the least chronic PM_2.5-related all-cause mortality numbers (<1 K mortalities). Throughout the study period, the PM_2.5-caused premature mortality decreased by 54 K, in which changes in PM_2.5 levels and baseline incidence rates of stroke and chronic obstructive pulmonary disease contributed to the positive effect, whereas demographic changes and baseline incidence rate change of ischemic heart disease and lung cancer brought a countervailing effect. Our findings suggest more dynamic and comprehensive policies and measures that take into account spatiotemporal variations of health burden for effective alleviation of the health impact of PM_2.5 pollution in the country.

Spatial Distribution of Primary and Secondary PM2.5 Concentrations Emitted by Vehicles in the Guanzhong Plain, China

With the rapid increase of the vehicle population in the Guanzhong Plain (GZP), the fine particulate matter (PM2.5) emitted by vehicles has an impact on regional air quality and public health. The spatial distribution of primary and secondary PM2.5 concentrations from vehicles in GZP in January and July 2017 was simulated in this study by using the Weather Research and Forecasting (WRF) model and the California Puff (CALPUFF) air quality model. The contributions of vehicle-related emission sources to total PM2.5 concentrations were also calculated. The results show that although the emissions of primary PM2.5, NOx, and SO2 in July were greater than those in January, the hourly average concentrations of primary and secondary PM2.5 in January were significantly higher than those in July. The highest concentrations of primary and total PM2.5 were mostly located in the urban areas of Xi’an and Xianyang in the central region of GZP. The contributions of exhaust emissions, secondary nitrates, brake wear, tire wear, and secondary sulfate to the total PM2.5 concentrations in GZP were 50.37%, 34.76%, 10.79%, 4.06%, and 0.04% in January and 71.91%, 11.14%, 11.89%, 5.03%, and 0.03% in July, respectively. These results will help us to further control PM2.5 pollution caused by vehicles.

Satellite-derived 1-km estimates and long-term trends of PM2.5 concentrations in China from 2000 to 2018

Exposure to ambient PM_2.5 (fine particulate matter) can cause adverse effects on human health. China has been experiencing dramatic changes in air pollution over the past two decades. Statistically deriving ground-level PM_2.5 from satellite aerosol optical depth (AOD) has been an emerging attempt to provide such PM_2.5 data for environmental monitoring and PM_2.5-related epidemiologic study. However, current countrywide datasets in China have generally lower accuracies with lower spatiotemporal resolutions because surface PM_2.5 level was rarely recorded in historical years (i.e., preceding 2013). This study aimed to reconstruct daily ambient PM_2.5 concentrations from 2000 to 2018 over China at a fine scale of 1 km using advanced satellite datasets and ground measurements. Taking advantage of the newly released Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1-km AOD dataset, we developed a novel statistical strategy by establishing an advanced spatiotemporal model relying on adaptive model structures with linear and non-linear predictors. The estimates in historical years were validated against surface observations using a strict leave-one-year-out cross-validation (CV) technique. The overall daily leave-one-year-out CV R² and root-mean-square-deviation values were 0.59 and 27.18 μg/m³, respectively. The resultant monthly (R² = 0.74) and yearly (0.77) mean predictions were highly consistent with surface measurements. The national PM_2.5 levels experienced a rapid increase in 2001-2007 and significantly declined between 2013 and 2018. Most of the discernable decreasing trends occurred in eastern and southern areas, while air quality in western China changed slightly in the recent two decades. Our model can deliver reliable historical PM_2.5 estimates in China at a finer spatiotemporal resolution than previous approaches, which could advance epidemiologic studies on the health impacts of both short- and long-term exposure to PM_2.5 at both a large and a fine scale in China.

Evidence that Ginkgo Biloba could use in the influenza and coronavirus COVID-19 infections

Coronavirus COVID-19 pandemic invades the world. Public health evaluates the incidence of infections and death, which should be reduced and need desperately quarantines for infected individuals. This article review refers to the roles of Ginkgo Biloba to reduce the risk of infection in the respiratory tract, the details on the epidemiology of corona COVID-19 and influenza, and it highlights how the Ginko Biloba could have been used as a novel treatment.Ginkgo Biloba can reduce the risk of infection by several mechanisms; these mechanisms involve Ginkgo Biloba contains quercetin and other constituents, which have anti-inflammatory and immune modulator effects by reducing pro-inflammatory cytokines concentrations. Cytokines cause inflammation which have been induced the injuries in lung lining.Some observational studies confirmed that Ginkgo Biloba reduced the risk of asthma, sepsis and another respiratory disease as well as it reduced the risk of cigarette smoking on respiratory symptoms. While other evidences suggested the characters of Ginkgo Biloba as an antivirus agent through several mechanisms. Ginkgolic acid (GA) can inhibit the fusion and synthesis of viral proteins, thus, it inhibit the Herpes Simplex Virus type1 (HSV-1), genome replication in Human Cytomegalovirus (HCMV) and the infections of the Zika Virus (ZIKV). Also, it inhibits the wide spectrum of fusion by inhibiting the three types of proteins that have been induced fusion as (Influenza A Virus [IAV], Epstein Barr Virus [EBV], HIV and Ebola Virus [EBOV]).The secondary mechanism of GA targeting inhibition of the DNA and protein synthesis in virus, greatly have been related to its strong effects, even afterward the beginning of the infection, therefore, it potentially treats the acute viral contaminations like (Measles and Coronavirus COVID-19). Additionally, it has been used topically as an effective agent on vigorous lesions including (varicella-zoster virus [VZV], HSV-1 and HSV-2). Ginkgo Biloba may be useful for treating the infected people with coronavirus COVID-19 through its beneficial effect. To assess those recommendations should be conducted with random control trials and extensive population studies.

Estimate hourly PM2.5 concentrations from Himawari-8 TOA reflectance directly using geo-intelligent long short-term memory network

Fine particulate matter (PM_2.5) has attracted extensive attention because of its baneful influence on human health and the environment. However, the sparse distribution of PM_2.5 measuring stations limits its application to public utility and scientific research, which can be remedied by satellite observations. Therefore, we developed a Geo-intelligent long short-term network (Geoi-LSTM) to estimate hourly ground-level PM_2.5 concentrations in 2017 in Wuhan Urban Agglomeration (WUA). We conducted contrast experiments to verify the effectiveness of our model and explored the optimal modeling strategy. It turned out that Geoi-LSTM with TOA reflectance, meteorological conditions, and NDVI as inputs performs best. The station-based cross-validation R², root mean squared error and mean absolute error are 0.82, 15.44 μg/m³, 10.63 μg/m³, respectively. Based on model results, we revealed spatiotemporal characteristics of PM_2.5 in WUA. Generally speaking, during the day, PM_2.5 concentration remained stable at a relatively high level in the morning and decreased continuously in the afternoon. While during the year, PM_2.5 concentrations were highest in winter, lowest in summer, and in-between in spring and autumn. Combined with meteorological conditions, we further analyzed the whole process of a PM_2.5 pollution event. Finally, we discussed the loss in removing clouds-covered pixels and compared our model with several popular models. Overall, our results can reflect hourly PM_2.5 concentrations seamlessly and accurately with a spatial resolution of 5 km, which benefits PM_2.5 exposure evaluations and policy regulations.

Spatio-temporal variations and trends of major air pollutants in China during 2015-2018

The Chinese government, as a policy response, has continued to invest efforts and resources to implement cost-effective air pollution control technologies and stringent regulation to reduce emissions from the most contributing sectors to protect the environment and public health. The higher density of monitoring stations (> 1600) distributed across China provides a timely opportunity to use them to study in detail the national pollution trends in light of more stringent air pollution control policies. In the present study, air quality datasets comprising hourly concentrations of PM2.5, O3, NO2, and SO2 collected across 1309, 1341, 1289, and 1347 monitoring stations respectively were obtained from the National Environmental Monitoring Centre over 4 years (2015-2018) and trend analysis was performed. Results indicate that the overall annual trends for PM2.5 and SO2 were - 2.9 ± 2.7 and - 3.2 ± 3.2 μg/m3/year, while the winter trends were - 4.8 ± 5.8 and - 6.9 ± 8.4 μg/m3/year respectively across China. The daily maximum 8-h average (DMA8) ozone concentration showed a significant positive trend of 2.4 ± 4.6 μg/m3/year, which was comparatively higher in summer at 4.4 ± 9.0 μg/m3/year. On the other side, NO2 trend is not great in number (- 0.45 ± 2.0 μg/m3/year). Overall, 62.2%, 61.8%, and 20.9% of PM2.5, SO2, and NO2 monitoring stations were associated with a negative trend of ≥ - 2 μg/m3/year. For O3 DMA8 concentrations, 50.7% of the monitoring stations showed a significant positive trend of ≥ 2 μg/m3/year. In light of the Chinese government's increasing impetus on combating air pollution and climate change via new policy regulations, it is important to understand the spatio-temporal distributions and relative contributions of the spectrum of gaseous pollutants to the pollution loads as well as identify changing emission loads across sectors. The results of this study will facilitate the formulation of evidence-based air pollution reduction strategies and policies.

Spatiotemporal distributions of surface ozone levels in China from 2005 to 2017: A machine learning approach

In recent years, ground-level ozone has become a severe ambient pollutant in major urban areas of China, which has adverse impacts on population health. However, in-situ measurements of the ozone concentration before 2013 in China are quite scarce, which cannot facilitate the assessment of the long-term trends and effects of ozone pollution. In this study, we used daily maximum 8-hour average (MDA8) ozone observations from 2013 to 2017 combined with concurrent ozone retrievals, aerosol reanalysis, meteorological parameters, and land-use data to establish a nationwide MDA8 prediction model based on the eXtreme Gradient Boosting (XGBoost) algorithm. The model achieves high prediction accuracy compared with other studies, with R² values for the by-year, site-based, and sample-based cross-validation (CV) schemes of 0.61, 0.64, and 0.78, respectively, at the daily level. External testing with regional measurements from 2005 to 2012 and nationwide data in 2018 have shown that the model is robust and reliable for historical data prediction, with external model testing R² values ranging from 0.60 to 0.87 at the month level in different years. Using the final estimator, we obtained nationwide monthly mean ozone concentrations from 2005 to 2012 and daily MDA8 ozone concentrations from 2013 to 2017 at a resolution of 0.1° × 0.1°. According to the average number of days exceeding the standard and the average of the 90th percentile of the MDA8 ozone concentrations, the Beijing-Tianjin-Hebei (BTH), the Yangtze River Delta, the Pearl River Delta, the Jianghan Plain, the Sichuan Basin, and the Northeast Plain regions were identified as pollution hotspots. During the research period, the overall ozone levels fluctuated slightly, and their trends were not spatially continuous. There was a significant increasing trend in the BTH region by 1.37 (95% CI: 0.46,2.29) μg/m³/year between 2013 and 2017. In 2017, 26.24% of the population lived in areas exceeding the Chinese grade II national air quality standard, which shows that ozone pollution has posed an obvious threat to population health in China. Our products will provide reliable support for future long-term nationwide health impact studies and policy-making for pollution control and prevention.

Substantial changes in PM2.5 pollution and corresponding premature deaths across China during 2015-2019: A model prospective

Long-term exposure to the ambient fine particulate matter (PM_2.5) is the major public health risk factor in China. Several past studies have assessed premature mortalities associated with PM_2.5 in China at varying levels of temporal and spatial scales using different methodological approaches. However, recently developed global exposure mortality model [GEMM NCD + LRI and GEMM 5-COD] provides a much more sophisticated methodology in capturing mortality due to PM_2.5-exposure than the commonly accepted integrated exposure-response (IER) model, which this study applied to China. This study provides a comparative assessment of the excess long-term PM_2.5-attributed nonaccidental deaths as well as cause-specific deaths for 349 cities in mainland China during five years (from 2015 to 2019) and compares the results with the spatial resolution scale of 0.1° × 0.1° across overall China. The results demonstrate that the national annual average PM_2.5 concentration declined from 51.9 ± 18.2 μg/m³ in 2015 to 39.0 ± 13.2 μg/m³ in 2019, and the overall annual negative trend was around -3.1 ± 2.2 μg/m³/year [-5.6 ± 3.4%/year] across China. Consequently, the number of PM_2.5-related deaths decreased by 383 thousand [95% CI: 331-429] to 1755 thousand [95% Confidence Interval: 1470-2025; GEMM NCD + LRI]; 315 thousand [95% CI: 227-370] to 1380 thousand [95% CI: 948-1740; GEMM 5-COD] and 125 thousand [95% CI: 64-140] to 876 thousand [95% CI: 394-1262; IER] in 2019, derived from the pre-established models (GEMM and IER). The estimate PM_2.5-attributed death with a spatial resolution of 0.1° × 0.1° was 2419 thousand [95% CI: 2041-2771; GEMM NCD + LRI], 1918 thousand [95% CI: 1333-2377; GEMM 5-COD] and 1162 thousand [95% CI: 534-1611; IER] in 2015, which is about 11-16% higher value than the city-level health risk assessment study. The estimated deaths by GEMM NCD + LRI and GEMM 5-COD were 104% and 61% higher than the estimated by IER, highlighting that total premature mortalities associated with PM_2.5 were substantially left behind based on the pre-existing model. The "other noncommunicable diseases" mortality, which IER method doesn't account for, was 375 thousand in 2019, 68 thousand less than in 2015. Such significant mortality was previously overlooked in estimation methods, which should now be considered for the air pollution-related policy development in China. The high number of premature deaths in central and northern parts of China, calls for the need for the Government to quickly impose even more stringent and effective pollution control measures.

Vitamin D in COVID - 19: Dousing the fire or averting the storm? - A perspective from the Asia-Pacific

COVID-19, the acute respiratory tract infection (RTI) caused by the Coronavirus, Sars-CoV-2, has swept around the world. No country has been spared from its onslaught. Treatments that can reduce the risk of infection and mortality from the disease are desperately needed. Though high quality randomized controlled trials are lacking, some observational and interventional studies that explore the link between vitamin D and RTIs exist. Vitamin D modulates both innate as well as adaptive immunity and may potentially prevent or mitigate the complications associated with RTIs. Evidence linking vitamin D to COVID-19 include that the outbreak occurred in winter in the northern hemisphere at a time when vitamin D levels are lowest in resident populations, that blacks and minority ethnic individuals who are known to have lower levels of vitamin D appear to be disproportionately affected and have more severe complications from the disease, that vitamin D deficiency has been shown to contribute to acute respiratory distress syndrome and that case fatality rates increase with age and in populations with comorbid conditions such as diabetes, hypertension, and cardiovascular disease, all of which are associated with lower vitamin D levels. This narrative review summarizes the current knowledge about the epidemiology and pathophysiology of COVID-19, the evidence linking vitamin D and RTIs, especially COVID-19, the mechanistic reasons behind the possible protective effect of vitamin D in COVID-19, and the evidence with regard to vitamin D supplementation in RTIs. It concludes with some recommendations regarding supplementation of vitamin D in patients with COVID-19.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D₃ for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40-60 ng/mL (100-150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D₃ doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.

Evidence that Vitamin D Supplementation Could Reduce Risk of Influenza and COVID-19 Infections and Deaths

The world is in the grip of the COVID-19 pandemic. Public health measures that can reduce the risk of infection and death in addition to quarantines are desperately needed. This article reviews the roles of vitamin D in reducing the risk of respiratory tract infections, knowledge about the epidemiology of influenza and COVID-19, and how vitamin D supplementation might be a useful measure to reduce risk. Through several mechanisms, vitamin D can reduce risk of infections. Those mechanisms include inducing cathelicidins and defensins that can lower viral replication rates and reducing concentrations of pro-inflammatory cytokines that produce the inflammation that injures the lining of the lungs, leading to pneumonia, as well as increasing concentrations of anti-inflammatory cytokines. Several observational studies and clinical trials reported that vitamin D supplementation reduced the risk of influenza, whereas others did not. Evidence supporting the role of vitamin D in reducing risk of COVID-19 includes that the outbreak occurred in winter, a time when 25-hydroxyvitamin D (25(OH)D) concentrations are lowest; that the number of cases in the Southern Hemisphere near the end of summer are low; that vitamin D deficiency has been found to contribute to acute respiratory distress syndrome; and that case-fatality rates increase with age and with chronic disease comorbidity, both of which are associated with lower 25(OH)D concentration. To reduce the risk of infection, it is recommended that people at risk of influenza and/or COVID-19 consider taking 10,000 IU/d of vitamin D₃ for a few weeks to rapidly raise 25(OH)D concentrations, followed by 5000 IU/d. The goal should be to raise 25(OH)D concentrations above 40-60 ng/mL (100-150 nmol/L). For treatment of people who become infected with COVID-19, higher vitamin D₃ doses might be useful. Randomized controlled trials and large population studies should be conducted to evaluate these recommendations.