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

Simulation and prediction of PM2.5 concentrations and analysis of driving factors using interpretable tree-based models in Shanghai, China

PM2.5 is a critical air pollutant, and understanding its drivers is essential for regional air quality control. This study employed meteorological and pollutant variables to predict PM2.5 concentrations in Shanghai using interpretable tree-based models. The random forest (RF) model performed best, achieving MAE, RMSE, MBE, and R2 values of 3.279, 4.609, 1.254, and 0.971, respectively, improving accuracy by 42.1%-85.5% compared to AdaBoost. Shapley additive explanations (SHAP) analysis identified CO, SO2, and O3 as the most influential factors. Partial dependence plots (PDPs) showed SO2 had the strongest impact below 40 μg/m³, while NO2 exhibited a linear positive correlation with PM2.5 up to 60 μg/m³. Atmospheric pressure and rainfall were negatively correlated with PM2.5, with notable reductions in concentrations under high-pressure conditions and rainfall levels between 0 and 20 mm. Temperature and relative humidity showed complex relationships, with sharp increases in PM2.5 at temperatures between -5 °C and 15 °C and SHAP values declining for humidity above 90%. Wind speed exhibited a non-linear effect, with minimal influence at higher velocities. The combined effects of different pollutants can be intensified significantly at higher levels. These findings offer valuable guidance for urban air quality management and pollution mitigation strategies.

Trends and drivers of aerosol vertical distribution over China from 2013 to 2020: Insights from integrated observations and modeling

Understanding aerosol vertical distribution is of great importance to climate change and atmospheric chemistry, but there is a dearth of systematical analysis for aerosol vertical distribution amid rapid emission decline after 2013 in China. Here, the GEOS-Chem model and multiple-sourced observations were applied to quantify the changes of aerosol vertical distributions in response to clean air actions. In 2013-2020, the MODIS aerosol optical depth (AOD) presented extensive decreasing trends by -7.9 %/yr to -4.2 %/yr in summer and -6.1 %/yr to -5.8 %/yr in winter in polluted regions. Vertically, the aerosol extinction coefficient (AEC) from CALIPSO decreased by -8.0 %/yr to -5.5 %/yr below ~1 km, but the trends weakened significantly with increasing altitude. Compared with available measurements, the model can reasonably reproduce 2013-2020 trends and seasonality in AOD and vertical AEC. Model simulations confirm that emission reduction was the dominant driver of the 2013-2020 decline in AOD, while the effect of meteorology varied seasonally, with contributions ranging from -2 % to 13 % in summer and -67 % to -2 % in winter. Vertical distributions of emission-driven AEC trends strongly depended on emission reductions, local planetary boundary layer height, and relative humidity. For aerosol components, sulfate accounted for ~50 % of the AOD decline during summer, followed by ammonium and organic aerosol, while in winter the contribution of organic aerosol doubled (24 %-35 %), and nitrate exhibited a weak increasing trend. Chemical production and meteorological conditions (e.g., relative humidity) primarily drove the nitrate contribution, but emission reduction and hygroscopicity were decisive for other components. This work provides an integrated observational and modeling effort to better understand rapid changes in aerosol vertical distribution over China.

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

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

Estimation of near-surface ozone concentration and analysis of main weather situation in China based on machine learning model and Himawari-8 TOAR data

Ozone (O₃) is an important greenhouse gas in the atmosphere. Stratospheric ozone protects human beings, but high near-surface ozone concentrations threaten environment and human health. Owing to the uneven distribution of ground-monitoring stations and the low time resolution of polar orbiting satellites, it is difficult to accurately evaluate the refinement and synergistic pollution of near-surface ozone in China. Besides, atmospheric circulation patterns also affect ozone concentrations greatly. In this study, a new generation of geostationary satellite is used to estimate the hourly near-surface ozone concentration with a spatial resolution of 0.05°. First, the Pearson correlation coefficient and maximum information coefficient were used to study the correlation between the top of atmospheric radiation (TOAR) of Himawari-8 satellite and O₃ concentration; seven TOAR channels were selected. Second, based on an interpretable deep learning model, the hourly ozone concentration in China from September 2015 to August 2021 was obtained using the TOAR-O₃ model. Finally, the self-organizing map method was used to determine six major summer weather circulation patterns in China. The results showed that (1) the near-surface O₃ concentration can be accurately estimated; the R² (RMSE: μg/m³) values of the daily, monthly, and annual tenfold cross validation results were 0.91 (12.74), 0.97 (5.64), and 0.98 (1.75), respectively. The feature importance of the model showed that the temperature, TOAR, and boundary layer height contributed 38 %, 22 %, and 13 %, respectively. (2) The O₃ concentration showed obvious spatiotemporal difference and gradually increased from 10:00 to 15:00 (Beijing time) every day. In most areas of China, O₃ concentration had increased significantly. (3) The O₃ concentration in northern China was the highest under the circulation pattern of the Meiyu front over the Yangtze River Delta, while in southern China, it was the highest under the circulation pattern of the northeast cold vortex controlling most of China.

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

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

Evaluation of organic aerosol filter sampling artefacts and implications to gravimetric PM2.5 mass at a COALESCE network site - Bhopal, India

Filter based PM_2.5 samples are typically used to measure its chemical constituents. Such measurements are made in dense sampling networks to assess regulatory compliance and for source apportionment. Thus, quantifying sampling artefacts is crucial. In this study, 24-h integrated PM_2.5 samples collected over Bhopal, India a COALESCE (CarbOnaceous AerosoL Emissions, Source apportionment and ClimatE impacts) site during 2019 and 2020, were used to estimate particulate organic carbon (OC) artefacts. Total OC and its thermal fractions (OC1, OC2, OC3, and OC4) measured on 349 bare quartz (Q) and QbQ filters each, were used to determine OC positive artefacts on quartz filters. 50 QbT (Quartz behind Teflon) filters in conjunction with the simultaneous QbQ samples (a subset of the total QbQ) were used to estimate OC volatilization from Teflon filters. On average, adsorbed gaseous OC contributed 17% and 11% to the measured total OC during 2019 and 2020, respectively. Further, the volatilization loss of organics from Teflon filter (used to quantify PM_2.5 mass) ranged between 7% and 9%, and 5% and 6% of the PM_2.5 mass during 2019 and 2020, respectively. The results of this study provide the first systematic long-term evaluation of thermal carbon fraction-wise sampling artefacts, estimates of organic volatilization losses from Teflon filters and their implications to PM_2.5 mass closure, over a regionally representative location in India.

Spatio-Temporal Characteristics of PM2.5 Concentrations in China Based on Multiple Sources of Data and LUR-GBM during 2016-2021

Fine particulate matter (PM2.5) has a continuing impact on the environment, climate change and human health. In order to improve the accuracy of PM2.5 estimation and obtain a continuous spatial distribution of PM2.5 concentration, this paper proposes a LUR-GBM model based on land-use regression (LUR), the Kriging method and LightGBM (light gradient boosting machine). Firstly, this study modelled the spatial distribution of PM2.5 in the Chinese region by obtaining PM2.5 concentration data from monitoring stations in the Chinese study region and established a PM2.5 mass concentration estimation method based on the LUR-GBM model by combining data on land use type, meteorology, topography, vegetation index, population density, traffic and pollution sources. Secondly, the performance of the LUR-GBM model was evaluated by a ten-fold cross-validation method based on samples, stations and time. Finally, the results of the model proposed in this paper are compared with those of the back propagation neural network (BPNN), deep neural network (DNN), random forest (RF), XGBoost and LightGBM models. The results show that the prediction accuracy of the LUR-GBM model is better than other models, with the R2 of the model reaching 0.964 (spring), 0.91 (summer), 0.967 (autumn), 0.98 (winter) and 0.976 (average for 2016-2021) for each season and annual average, respectively. It can be seen that the LUR-GBM model has good applicability in simulating the spatial distribution of PM2.5 concentrations in China. The spatial distribution of PM2.5 concentrations in the Chinese region shows a clear characteristic of high in the east and low in the west, and the spatial distribution is strongly influenced by topographical factors. The seasonal variation in mean concentration values is marked by low summer and high winter values. The results of this study can provide a scientific basis for the prevention and control of regional PM2.5 pollution in China and can also provide new ideas for the acquisition of data on the spatial distribution of PM2.5 concentrations within cities.

A three-dimensional LUR framework for PM2.5 exposure assessment based on mobile unmanned aerial vehicle monitoring

Land use regression (LUR) models have been widely used in epidemiological studies and risk assessments related to air pollution. Although efforts have been made to improve the performance of LUR models so that they capture the spatial heterogeneity of fine particulate matter (PM_2.5) in high-density cities, few studies have revealed the vertical differences in PM_2.5 exposure. This study proposes a three-dimensional LUR (3-D LUR) assessment framework for PM_2.5 exposure that combines a high-resolution LUR model with a vertical PM_2.5 variation model to investigate the results of horizontal and vertical mobile PM_2.5 monitoring campaigns. High-resolution LUR models that were developed independently for daytime and nighttime were found to explain 51% and 60% of the PM_2.5 variation, respectively. Vertical measurements of PM_2.5 from three regions were first parameterized to produce a coefficient of variation for the concentration (CVC) to define the rate at which PM_2.5 changes at a certain height relative to the ground. The vertical variation model for PM_2.5 was developed based on a spline smoothing function in a generalized additive model (GAM) framework with an adjusted R² of 0.91 and explained 92.8% of the variance. PM_2.5 exposure levels for the population in the study area were estimated based on both the LUR models and the 3-D LUR framework. The 3-D LUR framework was found to improve the accuracy of exposure estimation in the vertical direction by avoiding exposure estimation errors of up to 5%. Although the 3-D LUR-based assessment did not indicate significant variation in estimates of premature mortality that could be attributed to PM_2.5, exposure to this pollutant was found to differ in the vertical direction. The 3-D LUR framework has the potential to provide accurate exposure estimates for use in future epidemiological studies and health risk assessments.

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

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

Pollution and Weather Reports: Using Machine Learning for Combating Pollution in Big Cities

Air pollution has become the most important issue concerning human evolution in the last century, as the levels of toxic gases and particles present in the air create health problems and affect the ecosystems of the planet. Scientists and environmental organizations have been looking for new ways to combat and control the air pollution, developing new solutions as technologies evolves. In the last decade, devices able to observe and maintain pollution levels have become more accessible and less expensive, and with the appearance of the Internet of Things (IoT), new approaches for combating pollution were born. The focus of the research presented in this paper was predicting behaviours regarding the air quality index using machine learning. Data were collected from one of the six atmospheric stations set in relevant areas of Bucharest, Romania, to validate our model. Several algorithms were proposed to study the evolution of temperature depending on the level of pollution and on several pollution factors. In the end, the results generated by the algorithms are presented considering the types of pollutants for two distinct periods. Prediction errors were highlighted by the RMSE (Root Mean Square Error) for each of the three machine learning algorithms used.