Ground-level Particulate Matter Research Articles

Effects of atmospheric particulate pollution on lung function of athletes

There is a knowledge gap on how ground-level particulate pollution affects labor productivity in emerging nations due to a lack of study, especially when it comes to outdoor work settings like couriers in the express delivery industry. Combining findings from two research projects, this paper examines the socioeconomic consequences of particulate matter and ground-level particulate pollution. Special panel dataset from China's express delivery companies are used, we study how particulate pollution affects courier productivity. The instrumental variable of our analysis was built by particulate pollution data from upwind towns. Moreover, a comparable rise in particulate levels during the 30 days caused a significant 23.7% decline in worker productivity. This draws attention to a neglected area of the economic effects of particulate pollution, especially in underdeveloped countries. Our results also highlight the wider health hazards connected to outdoor activities in high-pollution locations, drawing comparisons on outdoor exercisers and particulate matter concentrations. The critical need for coordinated policy attention addressing both ground-level Particulate and particle matters in developing nations is highlighted by the increased risk of lung function impairment among outdoor exercisers owing to excessive particulate matter concentrations. The interrelated risks that air pollution poses to public health and economic productivity are clarified by this Comprehensive viewpoint.

Concentrated fireworks display-induced changes in aerosol vertical characteristics and atmospheric pollutant emissions

Fireworks affect the atmospheric environment and human health due to the rapid release of high levels of air pollutants. It is crucial to clarify the vertical characteristics of air pollutants and quantify the emission quantity for accurate control and health assessment. With the use of an unmanned aerial vehicle and a ground-based LiDAR system, we investigated the vertical profiles of particulate matter (PM) under the policy of concentrated fireworks detonation in a megacity (i.e., Changchun) in Northeast China and developed emission factors for fireworks displays. The results showed that the PM2.5, PM10, SO2 concentrations within a height of 200 m in the core area of fireworks display significantly increased by 143.8 %, 63.7 % and 174.3 %, respectively, on the day of the Lantern Festival relative to the background concentrations. There was a decreasing trend in the PM2.5 concentration with increasing height, with a maximum vertical decrease of 64 μg/m3 per 100 m. Despite a general decline in the concentration, the vertical distribution of polluted air masses exhibited heterogeneity, occasionally exhibiting a bimodal distribution pattern. Moreover, the correlation coefficients between the aerosol optical depth (AOD) at different altitudes (0.2–0.4, 0.4–0.7, and 0.7–1.0 km) and the ground-level PM concentration were 0.29, −0.35, and −0.35, respectively. This phenomenon indicates a reduced influence of ground-level PM emission on the AOD at altitudes from 0.4 to 1.0 km and the increased potential for aerosol particles to escape upwards, particularly in the troposphere. Furthermore, an approach combining real-time stereoscopic monitoring of PM and downwind wind speed was developed to calculate the estimated firework emission factors. The emission factors recorded during the Lantern Festival, specifically between 20:00 and 24:00, were 128.6 ± 28.3 mg/m2 per 10 min and 92.7 ± 23.2 mg/m2 per 10 min for PM2.5 and PM10, respectively, including an uncertainty of 9 %–14 % generated by the sensors. The proposed approach and corresponding emission factors could be used to easily quantify air pollutant emissions originating from fireworks displays and to conduct further numerical simulations for air pollution regulation and health assessment.

Ground-Level Particulate Matter (PM2.5) Concentration Mapping in the Central and South Zones of Peninsular Malaysia Using a Geostatistical Approach

Fine particulate matter is one of the atmospheric contaminants that exist in the atmosphere. The purpose of this study is to evaluate spatial–temporal changes in PM2.5 concentrations in the central and south zones of Peninsular Malaysia from 2019 to 2020. The study area involves twenty-one monitoring stations in the central and south zones of Peninsular Malaysia, using monthly and annual means of PM2.5 concentrations. The spatial autocorrelation of PM2.5 is calculated using Moran’s I, while three semi-variogram models are used to measure the spatial variability of PM2.5. Three kriging methods, ordinary kriging (OK), simple kriging (SK), and universal kriging (UK), were used for interpolation and comparison. The results showed that the Gaussian model was more appropriate for the central zone (MSE = 14.76) in 2019, while the stable model was more suitable in 2020 (MSE = 19.83). In addition, the stable model is more appropriate for both 2019 (MSE = 12.68) and 2020 (8.87) for the south zone. Based on the performance indicator, universal kriging was chosen as the best interpolation method in 2019 and 2020 for both the central and south zone. In conclusion, the findings provide a complete map of the variations in PM2.5 for two different zones, and show that interpolation methods such as universal kriging are beneficial and could be extended to the investigation of air pollution distributions in other areas of Peninsular Malaysia.

Changes in tropospheric air quality related to the protection of stratospheric ozone in a changing climate

Ultraviolet (UV) radiation drives the net production of tropospheric ozone (O3) and a large fraction of particulate matter (PM) including sulfate, nitrate, and secondary organic aerosols. Ground-level O3 and PM are detrimental to human health, leading to several million premature deaths per year globally, and have adverse effects on plants and the yields of crops. The Montreal Protocol has prevented large increases in UV radiation that would have had major impacts on air quality. Future scenarios in which stratospheric O3 returns to 1980 values or even exceeds them (the so-called super-recovery) will tend to ameliorate urban ground-level O3 slightly but worsen it in rural areas. Furthermore, recovery of stratospheric O3 is expected to increase the amount of O3 transported into the troposphere by meteorological processes that are sensitive to climate change. UV radiation also generates hydroxyl radicals (OH) that control the amounts of many environmentally important chemicals in the atmosphere including some greenhouse gases, e.g., methane (CH4), and some short-lived ozone-depleting substances (ODSs). Recent modeling studies have shown that the increases in UV radiation associated with the depletion of stratospheric ozone over 1980–2020 have contributed a small increase (~ 3%) to the globally averaged concentrations of OH. Replacements for ODSs include chemicals that react with OH radicals, hence preventing the transport of these chemicals to the stratosphere. Some of these chemicals, e.g., hydrofluorocarbons that are currently being phased out, and hydrofluoroolefins now used increasingly, decompose into products whose fate in the environment warrants further investigation. One such product, trifluoroacetic acid (TFA), has no obvious pathway of degradation and might accumulate in some water bodies, but is unlikely to cause adverse effects out to 2100.Graphical abstract

The Ground-Level Particulate Matter Concentration Estimation Based on the New Generation of FengYun Geostationary Meteorological Satellite

The new-generation FengYun geostationary meteorological satellite has a high spatial and temporal resolution, which is advantageous in environmental assessments and air pollution monitoring. This study researched the ground-level particulate matter concentration estimation, based on satellite-observed radiations. The radiation of ground-level particulate matter is separate from the apparent radiation observed by satellites. The positive correlation between PM2.5 and PM10 is also considered to improve the accuracy of inversion results and the interpretability of the estimation model. Then, PM2.5 and PM10 concentrations were estimated synchronously every 5 min in mainland China based on FY-4A satellite directly observed radiations. The validation results showed that the improved model estimated results were close to the ground site measured results, with a high determination coefficient (R2) (0.89 for PM2.5, and 0.90 for PM10), and a small Root Mean Squared Error (RMSE) (4.69 μg/m3 for PM2.5 concentrations, and 13.77 μg/m3 for PM10 concentrations). The estimation model presented a good performance in PM2.5 and PM10 concentrations during typical haze and dust storm cases, indicating that it is applicable in different weather conditions and regions.

Fast and operational gap filling in satellite-derived aerosol optical depths using statistical techniques

Satellite observations, used worldwide in the atmospheric sciences, are extremely useful for providing aerosol information within a wide spatial range. However, the coverage of aerosol data by satellite observations is sometimes of inferior quality because of the effects of surface reflectivity and clouds. To fill the gaps in aerosol optical depths (AODs) retrieved from geostationary ocean color imager observations, this study applies operational statistical techniques, including radial basis functions (RBFs) with four different weightings (i.e., linear, multiquadric, thin-plate, and inverse), Poisson, and ordinary Kriging. Based on computation time and accuracy of the individual gap-filling techniques, Poisson and the liner RBF are selected as the two best methods and then averaged with weights using one-dimensional weighted average (1D-WAVG) and two-dimensional weighted average (2D-WAVG) root mean square errors. All methods produce reliable results, yielding a correlation coefficient between 0.74 and 0.87 over the entire research domain. Out of the individual techniques, the Poisson, with an initial estimation from a zonal mean of AODs, is the most accurate with the lowest computational costs, even for a large number of missing pixels and most regions, excluding East China (EC). The Poisson’s high bias over EC is compensated in 1D- and 2D-WAVGs by taking more accurate estimations of the linear RBF than those of the Poisson over the region. If we consider 1D- and 2D-WAVGs in our analysis, the highest correlation is obtained from the 2D-WAVG over all regions. Because of its reliability and fast computation time, applying the 2D-WAVG can be a good solution to provide spatial–temporal continuous aerosol information. In addition to air pollution studies, such as real-time air quality predictions, estimation of ground-level particulate matter concentrations, and other applications, the fast and operational gap-filling technique can also be expanded to remote sensing data obtained from satellite observations to provide helpful and useful information for the public.

Air Quality Interpretation of Four Geographically Distinct Hot Spot States of India-An Appraisal Using Aerosol Optical Depth and Particulate Matter

The application of Moderate Resolution Imaging Spectroradiometer-Aerosol Optical Depth (MODIS-AOD) for Air Quality (AQ) assessment has contributed significantly by making use of a spatiotemporal relationship between ground-level particulate matter and aerosol optical density at both city and state levels. Traditional ground-based site-specific particulate matter monitoring method has found a profound gap in their spatial coverage. Such challenging situations prevail over the progressive development of the combined usage of ground monitoring technique along with the MODIS-AOD product (with 10 km spatial resolution). This was achieved and derived by assembling the satellite sensor, Terra as a tool for the regional-based specific studies for monitoring airborne particles. Their analytical linear association was evaluated and explored their feasibility for monitoring either Respirable Suspended organic Matter (RSPM) or AQI in spatiotemporally distinct geographical sites situated in four States of India. Examined the interrelationship between RSPM-AOD against meteorological drivers through Principal Component Analysis (PCA) and Quantitative Risk Assessment (QRA) of the focussed four zones. Elevated Air Pollution was noticed in the urban and industrial cities of Pune, Kochi, and Chennai. They were flanked by various industrial clusters and the substantial personal handling of vehicles. A region-wise RSPM-AOD correlation was recorded for the categorized areas: WS1, WS2, SW, and ES. Each zone was subsequently judged and a noteworthy association was linked between the ground-monitored RSPM data and the retrieved AOD output. This means of records could represent the aerosol component; consequently, infer their sources of origin and in later days could come up with reasonable emission control channels. States of India Maharashtra, Karnataka, Kerala, and Tamil Nadu, are categorized as WS1, WS2, SW, and ES respectively.

Geostationary satellite-derived ground-level particulate matter concentrations using real-time machine learning in Northeast Asia

Rapid economic growth, industrialization, and urbanization have caused frequent air pollution events in East Asia over the last few decades. Recently, aerosol data from geostationary satellite sensors have been used to monitor ground-level particulate matter (PM) concentrations hourly. However, many studies have focused on using historical datasets to develop PM estimation models, often decreasing their predictability for unseen data in new days. To mitigate this problem, this study proposes a novel real-time learning (RTL) approach to estimate PM with aerodynamic diameters of <10 μm (PM10) and <2.5 μm (PM2.5) using hourly aerosol data from the Geostationary Ocean Color Imager (GOCI) and numerical model outputs for daytime conditions over Northeast Asia. Three schemes with different weighting strategies were evaluated using 10-fold cross-validation (CV). The RTL models, which considered both concentration and time as weighting factors (i.e., Scheme 3) yielded consistent improvement for 10-fold CV performance on both hourly and monthly scales. The real-time calibration results for PM10 and PM2.5 were R2 = 0.97 and 0.96, and relative root mean square error (rRMSE) = 12.1% and 12.0%, respectively, and the 10-fold CV results for PM10 and PM2.5 were R2 = 0.73 and 0.69 and rRMSE = 41.8% and 39.6%, respectively. These results were superior to results from the offline models in previous studies, which were based on historical data on an hourly scale. Moreover, we estimated PM concentrations in the ocean without using land-based variables, and clearly demonstrated the PM transport over time. Because the proposed models are based on the RTL approach, the density of in-situ monitoring sites could be a major uncertainty factor. This study identified that a high error occurred in low-density areas, whereas a low error occurred in high-density areas. The proposed approach can be operated to monitor ground-level PM concentrations in real-time with uncertainty analysis to ensure optimal results.

Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.

Estimation of ground-level particulate matter concentrations based on synergistic use of MODIS, MERRA-2 and AERONET AODs over a coastal site in the Eastern Mediterranean

Satellite-derived aerosol optical depth (AOD) products are widely used to estimate the spatial and temporal characteristics of ground-level particulate matters (PM). Satellite-based approaches in the operational use of PM estimation models have limitations such as missing values in satellite-based variables due to the impact of cloud cover, relative humidity, and aerosol vertical distribution. Therefore, the spatial-temporal resolution of the estimation models is forced to be improved by using AOD products having different temporal resolutions from different platforms. In this study, ground-based PM10 concentrations were aimed to be estimated using different gap-filled AOD datasets between 2008 and 2016 over a coastal site in the Eastern Mediterranean. For the estimation of PM10 concentrations, daily AOD data were mainly used from MODIS. Then, two different gap-filled MODIS AOD datasets with both AERONET and MERRA-2 AODs were also examined separately on both multi-annual and multi-seasonal basis since the number of MODIS AOD data was temporally limited in the region. A pattern recognition neural network (PRNN) model was used for the estimation of PM10 concentrations. AOD with several meteorological parameters from an on-site meteorological station and aerosol diagnostic products from MERRA-2 were used as inputs to the estimation model. The most significant variables for the model were identified in 23 independent variables using the multiple linear regression (MLR). The results indicated that the best estimation (R = 0.74) was obtained with the gap-filled AODMODIS+MERRA dataset for the period covering all years whereas the AODMODIS dataset alone showed the poorest performance (R = 0.62). However, the performance of the gap-filled datasets varied with the seasons. For example, the AODMODIS dataset alone showed the best estimation performance (R = 0.67) in the summer whereas the gap-filled AODMODIS+AERONET dataset had the best performance (R = 0.59) in the winter. Overall results suggest that for estimating ground-level PM concentrations, an approach of gap-filled AOD dataset usage for estimation models is useful especially in rainy seasons such as the winter and autumn where MODIS AOD retrievals are limited.

Current advances and future challenges of AIoT applications in particulate matters (PM) monitoring and control

Air pollution is at the center of pollution-control discussion due to the significant adverse health effects on individuals and the environment. Research has shown the association between unsafe environments and different sizes of particulate matter (PM), highlighting the importance of pollutant monitoring to mitigate its detrimental effect. By monitoring air quality with low-cost monitoring devices that collect massive observations, such as Air Box, a comprehensive collection of ground-level PM concentration is plausible due to the simplicity and low-cost, propelling applications in agriculture, aquaculture, and air quality, water resources, and disaster prevention. This paper aims to view IoT-based systems with low-cost microsensors at the sensor, network, and application levels, along with machine learning algorithms that improve sensor networks’ precision, providing better resolution. From the analysis at the three levels, we analyze current PM monitoring methods, including the use of sensors when collecting PM concentrations, demonstrate the use of IoT-based systems in PM monitoring and its challenges, and finally present the integration of AI and IoT (AIoT) in PM monitoring, indoor air quality control, and future directions. In addition, the inclusion of Taiwan as a site analysis was illustrated to show an example of AIoT in PM-control policy-making potential directions.

Temporal and spatial variations of aerosol optical properties over the Korean peninsula during KORUS-AQ

We investigated the temporal and spatial variations of aerosol optical properties over the Korean peninsula during the KORUS-AQ (KORea–United States Air Quality) experiment with ground-based aerosol optical properties measured by remote and in-situ techniques. On the ground, AErosol RObotic NETwork (AERONET) and ground-level particulate matter (PM) concentration from air quality monitoring stations were used. From the NASA DC-8 research aircraft, the airborne Spectrometer for Sky-Scanning, Sun-Tracking Atmospheric Research instrument (4STAR), an aerosol mass spectrometer (AMS), and a single particle soot photometer (SP2) provided aerosol information. Average hourly fine- and coarse-mode aerosol optical depth (AOD) and fine mode fraction (FMF), were divided into four clusters (background, Seoul Metropolitan Area (SMA), southwest, and east) representing different temporal/spatial variations; the results of those clusters were similar to the clustering results using PM from air quality monitoring stations. The downwind region of SMA was dominant by light-scattering fine-mode aerosols likely due to secondary aerosol formation with high fine-mode AOD and single scattering albedo compared to other regions, even Seoul. The fine-mode aerosols were more spatially homogeneous than coarse-mode aerosols, especially in the west-to-east direction, because fine-mode aerosols are usually transported on a regional scale by westerlies rather than emitted from local emission sources. However, the aerosol size distribution was spatially more homogeneous because of a consistent contribution of fine- and coarse-mode AOD to total AOD regardless of direction between the AERONET sites. During high-aerosol loading episodes, the temporal and spatial variations of aerosol optical properties were similar to those derived from 4STAR and ground-level PM concentrations, providing detailed information on aerosol behavior and characteristics. Using missed-approach flight segments (touchdown and take-off without a full-stop), investigation of diurnal variations over the SMA revealed a significant increase in AOD and Angstrom exponent (AE) in the afternoon compared to morning and noon, especially in the downwind region, because of more active secondary formation resulting from advected and/or emitted local pollutants and precursors. The diurnal variation of PM1 in the downwind region was similar to that of AOD; it was mainly increased during the day by secondary organic aerosols and ammonium nitrate due to large amounts of isoprene and meteorological conditions that supported secondary aerosol formation. Ammonium sulfate also partially led to increasing the PM1, but its behavior was unclear, because RH decreased during the day, which would imply the reduced aqueous-phase oxidation (the major pathway for sulfate production). Compared to the downwind region, ammonium nitrate and refractory black carbon decreased in Seoul because of a slightly decreased traffic volume, dissociation of ammonium nitrate, and increased boundary layer height according to meteorological conditions in the afternoon. The results of this study provide detailed information on aerosol behavior given the synergy of the various measurement platforms used. Along with the evidence of active photochemical reactions taking place in the downwind region, such data will be useful in formulating policies that improve air quality in Korea.

Estimation of ambient PM2.5 in Iraq and Kuwait from 2001 to 2018 using machine learning and remote sensing

Iraq and Kuwait are in a region of the world known to be impacted by high levels of fine particulate matter (PM2.5) attributable to sources that include desert dust and ambient pollution, but historically have had limited pollution monitoring networks. The inability to assess PM2.5 concentrations have limited the assessment of the health impact of these exposures, both in the native populations and previously deployed military personnel. As part of a Department of Veterans Affairs Cooperative Studies Program health study of land-based U.S. military personnel who were previously deployed to these countries, we developed a novel approach to estimate spatially and temporarily resolved daily PM2.5 exposures 2001–2018. Since visibility is proportional to ground-level particulate matter concentrations, we were able to take advantage of extensive airport visibility data that became available as a result of regional military operations over this time period. First, we combined a random forest machine learning and a generalized additive mixed model to estimate daily high resolution (1 km × 1 km) visibility over the region using satellite-based aerosol optical depth (AOD) and airport visibility data. The spatially and temporarily resolved visibility data were then used to estimate PM2.5 concentrations from 2001 to 2018 by converting visibility to PM2.5 using empirical relationships derived from available regional PM2.5 monitoring stations. We adjusted for spatially resolved meteorological parameters, land use variables, including the Normalized Difference Vegetation Index, and satellite-derived estimates of surface dust as a measure of sandstorm activity. Cross validation indicated good model predictive ability (R2 = 0.71), and there were considerable spatial and temporal differences in PM2.5 across the region. Annual average PM2.5 predictions for Iraq and Kuwait were 37 and 41 μg/m3, respectively, which are greater than current U.S. and WHO standards. PM2.5 concentrations in many U.S. bases and large cities (e.g. Bagdad, Balad, Kuwait city, Karbala, Najaf, and Diwaniya) had annual average PM2.5 concentrations above 45 μg/m3 with weekly averages as high as 150 μg/m3 depending on calendar year. The highest annual PM2.5 concentration for both Kuwait and Iraq were observed in 2008, followed by 2009, which was associated with extreme drought in these years. The lowest PM2.5 values were observed in 2014. On average, July had the highest concentrations, and November had the lowest values, consistent with seasonal patterns of air pollution in this region. This is the first study that estimates long-term PM2.5 exposures in Iraq and Kuwait at a high resolution based on measurements data that will allow the study of health effects and contribute to the development of regional environmental policies. The novel approach demonstrated may be used in other parts of the world with limited monitoring networks.

Exploring the relationship between high-resolution aerosol optical depth values and ground-level particulate matter concentrations in the Metropolitan Area of São Paulo

The spatiotemporal pattern of particulate matter (PM) concentrations is an important factor in predicting health issues in inhabitants of urban areas. The integration of satellite-derived aerosol optical depth (AOD) data with ground-level PM concentration data, obtained from monitoring networks, has contributed to better characterization of the spatiotemporal variability of aerosols worldwide. However, before using satellite AOD data as a proxy for PM in epidemiological and air quality studies in specific regions, the applicability of that strategy must be evaluated. In this study, we evaluate the use of the high-resolution AOD, derived from Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm, as a predictor of surface PM concentrations in the Metropolitan Area of São Paulo (MASP). We found relatively weak or negative correlations between PM concentrations and MAIAC AOD, even after vertical correction by planetary boundary layer height and the hygroscopic growth factor. The weak correlations reported in this study are mainly due to the mismatch between the current MAIAC aerosol model and the properties of local aerosols in the MASP. Our results suggest that sources of aerosol particles in the MASP are quite diverse and that there is therefore no single optical model suitable for use with satellite-derived AOD.

Using Kriging incorporated with wind direction to investigate ground-level PM2.5 concentration

Conventional interpolation methods, such as spatial averaging, nearest neighbor, inverse distance weight and ordinary Kriging (OK); for estimating the spatial distribution of ground-level particulate matter (PM) data, do not account for the wind direction for estimating the spatial distribution of PM2.5. In this work, an interpolation algorithm, Win-OK accounting for the wind direction, is developed. In contrast to ordinary Kriging where all locations (irrespective of the wind direction) in the vicinity of a site is considered, the new algorithm (Win-OK) predicts the value at a certain location based on the measured values at locations upwind as determined by the wind direction. This new methodology, Win-OK is validated by applying it to analyze the hourly spatial distribution of ground-level PM2.5 concentrations during Chinese New Year and Chinese National Day in 2017 in Xinxiang city, China. The performance of OK and Win-OK are compared by using them to build PM2.5 concentration heat-maps. A “leave-one-out” cross validation methodology is used to calculate the root-mean-square error (RMSE) and standard deviation for evaluating both algorithms. The results show that OK sometimes gives an extremely high RMSE value using a Gaussian semi-variance model, and the standard deviation significantly deviates from the measured values. Win-OK was found to more accurately predict the PM2.5 spatial distribution in a specific sector. The performance of Win-OK is more stable than OK as established by comparing the calculated RMSE and standard deviation from predictions of both algorithms. Win-OK with a spherical semi-variance model is the most accurate method investigated here for deriving the spatial distribution of ground-level PM2.5. The new algorithm developed here could improve the prediction accuracy of PM2.5 spatial distribution by considering the effect of wind direction.

Effects of Low‐Carbon Energy Adoption on Airborne Particulate Matter Concentrations With Feedbacks to Future Climate Over California

AbstractCalifornia plans to reduce emissions of long‐lived greenhouse gases (GHGs) through adoption of new energy systems that will also lower concentrations of short‐lived absorbing soot contained in airborne particulate matter (PM). Here we examine the direct and indirect effects of reduced PM concentrations under a low‐carbon energy (GHG‐Step) scenario on radiative forcing in California. Simulations were carried out using the source‐oriented WRF/Chem (SOWC) model with 12 km spatial resolution for the year 2054. The avoided aerosol emissions due to technology advances in the GHG‐Step scenario reduce ground level PM concentrations by ~8.85% over land compared to the Business‐as‐Usual (BAU) scenario, but changes to meteorological parameters are more modest. Top‐of‐atmospheric forcing predicted by the SOWC model increased by 0.15 W m−2, surface temperature warmed by 0.001 K, and planetary boundary layer height (PBLH) increased by 2.20 cm in the GHG‐Step scenario compared to the BAU scenario. PM climate feedbacks are small because the significant changes in ground level PM concentrations associated with the GHG‐Step scenario are limited to the first few hundred meters of the atmosphere, with little change for the majority of the vertical column above that level. As an order‐of‐magnitude comparison, the long‐term effects of global reductions in GHG emissions (RCP8.5–RCP4.5) lowered average surface temperature over the California study domain by approximately 0.76 K. The effects of long‐lived climate pollutants such as CO2 are much stronger than the effects of short‐lived climate pollutants such as PM soot over California in the year 2054.

Impact of environmental pollution on the retrieval of hourly aerosol products from Advanced Himawari Imager (AHI) over Beijing

Advanced Himawari Imager (AHI) aboard Himawari-8 provides hourly Aerosol Optical Thickness (AOT) products, widely used to assimilation models and ground-level particulate matter (PM) concentration retrievals. However, the performance of AHI AOT products remains unclear under different air quality conditions. In this study, we evaluate the performance of the AHI hourly AOT products with ground-based AOT observations from four Aerosol Robotic NETwork (AERONET) sites for 2018 over Beijing under different pollution levels and aerosol types. Near-surface PM concentrations are used to categorize air quality as clean, moderate, and heavy pollution, and the aerosol types (biomass burning and urban/industrial (BMA_UIA), maritime (MA), dust (DA), mixed-type (MIXA)) are classified based on the threshold limits of AERONET aerosol properties. Overall, the AHI hourly AOT achieves good consistency with AERONET measurements in Beijing (correlation coefficient (R) = 0.78 and a root-mean-square-error (RMSE) = 0.29). Under clean conditions, the AOT successful retrieval rate is more than 80%, while it is deficient for moderate and heavy pollution. AOT for clean and moderate pollution performs better than that for heavy pollution. Moreover, AHI AOT retrievals for fine mode aerosol (BMA_UIA) are in good agreement with AERONET observations, and the performance is better than that under coarse mode aerosol (MA and DA) conditions. AHI AOT may capture the spatiotemporal distribution of fine particles pollution over Beijing. These findings will provide useful information for the improvement of the aerosol model of the retrieval algorithm and the monitoring of urban fine particles pollution.

Assessment of the short-term mortality effect of the national action plan on air pollution in Beijing, China

The Air Pollution Prevention and Control Action Plan (referred to as the Action Plan henceforth) provides a golden opportunity for China to evaluate whether the remarkable air quality improvements as a result of the plan have brought about health benefits to residents. Based on the ground-level particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) concentrations and the daily respiratory–cardiovascular mortality, we aimed to assess changes in the mortality effect associated with short-term exposure to PM2.5 due to the implementation of the Action Plan in Beijing. We analyzed the changes in PM2.5 concentrations and air quality during the pre- and post-emission reduction periods. We then used the generalized additive model to estimate the changes in mortality risk associated with PM2.5 exposure during both periods. We found that following the introduction of the Action Plan, the annual average PM2.5 concentrations declined from 101.7 μg m−3 in 2013 to 58.6 μg m−3 in 2017, attaining the target of the plan (60 μg m−3). The remarkable reduction in PM2.5 concentrations has led to a marked decrease in mortality risk. Compared with the pre-emission reduction level, total respiratory mortality decreased from 0.56% (95% CI: 0.40%–0.73%) to 0.43% (95% CI: 0.23%–0.63%), while the total cardiovascular mortality decreased from 0.44% (95% CI: 0.37%–0.52%) to 0.29% (95% CI: 0.19%–0.39%). Significant decreases were also observed in sex-specific subgroups. Our study implied that the significant efforts of the nation to clean China’s air has yielded positive results of air quality improvement and human health protection in Beijing. However, the ambient air pollution in Beijing remains severe. The PM2.5 concentrations still exceed the level (annual average of 10 μg m−3) recommended by the World Health Organization. Thus, consistent efforts are required to implement the emission abatement measures to continuously curb air pollution and protect public health.

Vertical Wind Shear Modulates Particulate Matter Pollutions: A Perspective from Radar Wind Profiler Observations in Beijing, China

Vertical wind shear (VWS) is one of the key meteorological factors in modulating ground-level particulate matter with an aerodynamic diameter of 2.5 µm or less (PM2.5). Due to the lack of high-resolution vertical wind measurements, how the VWS affects ground-level PM2.5 remains highly debated. Here we employed the wind profiling observations from the fine-time-resolution radar wind profiler (RWP), together with hourly ground-level PM2.5 measurements, to explore the wind features in the planetary boundary layer (PBL) and their association with aerosols in Beijing for the period from December 1, 2018, to February 28, 2019. Overall, southerly wind anomalies almost dominated throughout the whole PBL or even beyond the PBL under polluted conditions during the course of a day, as totally opposed to the northerly wind anomalies in the PBL under clean conditions. Besides, the ground-level PM2.5 pollution exhibited a strong dependence on the VWS. A much weaker VWS was observed in the lower part of the PBL under polluted conditions, compared with that under clean conditions, which could be due to the strong ground-level PM2.5 accumulation induced by weak vertical mixing in the PBL. Notably, weak northbound transboundary PM2.5 pollution mainly appeared within the PBL, where relatively small VWS dominated. Above the PBL, strong northerlies winds also favored the long-range transport of aerosols, which in turn deteriorated the air quality in Beijing as well. This was well corroborated by the synoptic-scale circulation and backward trajectory analysis. Therefore, we argued here that not only the wind speed in the vertical but the VWS were important for the investigation of aerosol pollution formation mechanism in Beijing. Also, our findings offer wider insights into the role of VWS from RWP in modulating the variation of PM2.5, which deserves explicit consideration in the forecast of air quality in the future.

Estimation of spatially continuous daytime particulate matter concentrations under all sky conditions through the synergistic use of satellite-based AOD and numerical models

Satellite-derived aerosol optical depth (AOD) products are one of main predictors to estimate ground-level particulate matter (PM10 and PM2.5) concentrations. Since AOD products, however, are only provided under high-quality conditions, missing values usually exist in areas such as clouds, cloud shadows, and bright surfaces. In this study, spatially continuous AOD and subsequent PM10 and PM2.5 concentrations were estimated over East Asia using satellite- and model-based data and auxiliary data in a Random Forest (RF) approach. Data collected from the Geostationary Ocean Color Imager (GOCI; 8 times per day) in 2016 were used to develop AOD and PM models. Three schemes (i.e. G1, A1, and A2) were proposed for AOD modeling according to target AOD data (GOCI AOD and AERONET AOD) and the existence of satellite-derived AOD. The A2 scheme showed the best performance (validation R2 of 0.74 and prediction R2 of 0.73 when GOCI AOD did not exist) and the resultant AOD was used to estimate spatially continuous PM concentrations. The PM models with location information produced successful estimation results with R2 of 0.88 and 0.90, and rRMSE of 26.9 and 27.2% for PM10 and PM2.5, respectively. The spatial distribution maps of PM well captured the seasonal and spatial characteristics of PM reported in the literature, which implies the proposed approaches can be adopted for an operational estimation of spatially continuous AOD and PMs under all sky conditions.