Spatial and temporal variability of PM2.5 concentration in China

Trace elements and microbial community composition associated with airborne PM2.5 in wetlands: A case study in Tablas de Daimiel National Park

Tablas de Daimiel National Park (TDNP) is one of the most important wetlands in the Iberian Peninsula. Due to its location near various cities and new industries focused on agricultural waste revalorization, we investigated concurrently the concentrations of particulate matter 2.5 (PM2.5) mass, trace element composition, and associated microbial communities (bacteria and fungi) during a year-long study. The goal of this study was to explore the dependencies among these physicochemical and microbiological parameters on a seasonal time scale. Additionally, we assessed meteorological conditions and back trajectories to shed light on atmospheric mechanisms and sources related to these elements. We found the variability of PM2.5 to be influenced by local meteorological parameters. Through the analysis of crustal enrichment factors (EFs), bivariate correlations, and air mass patterns, we determined that soil resuspension was the primary contributor to elevated metal concentrations in PM2.5 within the park, followed by other minor sources, such as traffic emissions and Sahara dust intrusions. The measured metal levels were used to calculate the ecological risk in the area, resulting in a low ecological risk index (RI) of 52. Shifts in microbial community structure were observed to be mainly driven by changes in air temperature and Cu concentration. The results from this study contribute to a better understanding of the environmental dynamics in TDNP. Taken together, our findings will aid in the development of effective strategies for its conservation and management.

Air quality trends and implications pre and post Covid-19 restrictions

Air pollution causes millions of premature deaths every year. Thus, air quality assessment is essential to preserve human health and support authorities to identify proper policies. In this study, concentration levels of 6 air contaminants (benzene, carbon monoxide, nitrogen dioxide, ground level ozone, particulate matters) as monitored in 2019, 2020 and 2021 by 37 stations, located in Campania (Italy) were analysed. Particular attention has been paid to March-April 2020 period to get clues on the possible effects of the lockdown regulations, imposed in Italy from March 9th to May 4th to limit COVID-19 spread, on atmospheric pollution. Air Quality Index (AQI), an algorithm developed by the United States Environmental Protection Agency (US-EPA), allowed us to classify the air quality from moderately unhealthy to good for sensitive groups. The evaluation of air pollution impact on human health by using the AirQ+ software evidenced a significant decrement of adult mortality in 2020 respect to 2019 and 2021. Among the six pollutants considered, PM₁₀ and PM_2.5 resulted the less affected by the lockdown restrictions. Finally, a comparison between NO₂ ground level concentration and the reprocessed Level 2 NO₂ tropospheric column concentration obtained from satellite surveys highlighted as concentration measured at the ground level stations can be strongly influenced by the station position and its surroundings.

Spatial and temporal characteristics of air pollutants and their health effects in China during 2019-2020

This work presents the temporal and spatial characteristics of the major air pollutants and their associated health risks in China from 2019 to 2020, by using the monitoring data from 367 cities. The annual average PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃ concentrations decreased by 10.9%, 13.2%, 9.3%, 10.1%, 9.4%, and 5.5% from 2019 to 2020. National average PM_2.5 concentration in 2020 met the standard of 35 μg/m³, and that of O₃ decreased from 2019. COVID-19 lockdown affected NO₂ level dramatically, yet influences on PM_2.5 and O₃ were less clear-cut. Positive correlations between PM_2.5 and O₃ were found, even in winter in all five key regions, e.g., Jing-Jin-Ji (JJJ), FenWei Plain (FWP), Yangtze River Delta (YRD), Pearl River Delta (PRD) and Chengdu-Chongqing Region (CCR), indicating importance of secondary production for both PM_2.5 and O₃. Large seasonal variability of PM_2.5-SO₂ correlation indicates a varying role of SO₂ to PM_2.5 pollution in different seasons; and generally weak correlations in winter between PM_2.5 and NO₂ or SO₂ reveal the complexity of secondary formation processes to PM_2.5 pollution in winter. Multilinear regression analysis between PM_2.5 and SO₂, NO₂ and CO demonstrates that PM_2.5 is more sensitive to the change of NO₂ than SO₂ in JJJ, FWP, PRD and CCR, suggesting a priority of NO_x emission control for future PM_2.5 reduction. Furthermore, the new World Health Organization Air Quality Guidelines (WHO AQG2021) were adopted to calculate the excess health risks (ER) as well as the health-risk based air quality index (HAQI_WHO) of the pollutants. Such assessment points out the severity of air pollution associated health risks under strict standards: 40.0% of days had HAQI_WHO>100, while only 14.4% days had AQI>100. PM_2.5 ER was generally larger than O₃ ER, but O₃ ER in low PM_2.5 region (PRD) and during summer became more serious. Notably, NO₂ ER became even more important than PM_2.5 due to its strict limit of WHO AQG2021. Overall, our results highlight the increasing importance of O₃ in both air quality evaluation and health risk assessment, and the importance of coordinated mitigation of multiple pollutants (mainly PM_2.5, O₃ and NO₂) in protecting the public health.

Spatio-Temporal Characteristics and Variation Pattern of the Atmospheric Particulate Matter Concentration: A Case Study of the Beijing–Tianjin–Hebei Region, China

Based on measurement data from air quality monitoring stations, the spatio-temporal characteristics of the concentrations of particles with aerodynamic equivalent diameters smaller than 2.5 and 10 μm (PM2.5 and PM10, respectively) in the Beijing–Tianjin–Hebei (BTH) region from 2015 to 2018 were analysed at yearly, seasonal, monthly, daily and hourly scales. The results indicated that (1) from 2015 to 2018, the annual average values of PM2.5 and PM10 concentrations and the PM2.5/PM10 ratio in the study area decreased each year; (2) the particulate matter (PM) concentration in winter was significantly higher than that in summer, and the PM2.5/PM10 ratio was highest in winter and lowest in spring; (3) the PM2.5 and PM10 concentrations exhibited a pattern of double peaks and valleys throughout the day, reaching peak values at night and in the morning and valleys in the morning and afternoon; and (4) with the use of an improved sine function to simulate the change trend of the monthly mean PM concentration, the fitting R2 values for PM2.5 and PM10 in the whole study area were 0.74 and 0.58, respectively. Moreover, the high-value duration was shorter, the low-value duration was longer, and the concentration decrease rate was slower than the increase rate.

Environmental implications of reduced electricity consumption in Wuhan during COVID-19 outbreak: A brief study

Due to the COVID-19 outbreak, Wuhan was locked down from 23 January 2020 to 8 April 2020, a total of 76 days. It is well known that the electricity consumption is a direct reflection of human activity. During the lockdown of Wuhan, most of human activities were forbidden. The reduction in human activity would inevitably lead to a reduction in electricity consumption. At the same time, anthropogenic emissions of air pollutants would also be reduced with the reduction of human activity. In this study, the correlation between electricity consumption and air pollutants during lockdown was discussed in detail. The result showed that the drop in pollutants concentrations in January should be attributed to the washout effect of rainfall rather than the lockdown. The decrease of electricity consumption in the secondary industry might play a significant role on the decrease of PM2.5 and NO2 concentrations in Wuhan in February 2020. The decrease in NO2 concentration in March should be attributed to the reduction of pollutants emissions from the tertiary industry, which means that more attention should be paid to the control of NO2 emission in the tertiary industry. Due to reduced emissions from local sources, the role of long-range transport sources might be more significant during the lockdown of Wuhan. By PSCF analysis, southeast of Wuhan could be the major potential emission sources of PM2.5 , especially in the northern part of Jiangxi province. It was suggested that stricter regulation of pollutants emissions should be implemented in this area.

A Novel Recursive Model Based on a Convolutional Long Short-Term Memory Neural Network for Air Pollution Prediction

Deep learning provides a promising approach for air pollution prediction. The existing deep learning-based predicted models generally consider either the temporal correlations of air quality monitoring stations or the nonlinear relationship between the PM2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) concentrations and explanatory variables. Spatial correlation has not been effectively incorporated into prediction models, therefore exhibiting poor performance in PM2.5 prediction tasks. Additionally, determining the manner by which to expand longer-term prediction tasks is still challenging. In this paper, to allow for spatiotemporal correlations, a spatiotemporal convolutional recursive long short-term memory (CR-LSTM) neural network model is proposed for predicting the PM2.5 concentrations in long-term prediction tasks by combining a convolutional long short-term memory (ConvLSTM) neural network and a recursive strategy. Herein, the ConvLSTM network was used to capture the complex spatiotemporal correlations and to predict the future PM2.5 concentrations; the recursive strategy was used for expanding the long-term prediction tasks. The CR-LSTM model was used to realize the prediction of the future 24 h of PM2.5 concentrations for 12 air quality monitoring stations in Beijing by configuring both the appropriate time lag derived from the temporal correlations and the spatial neighborhood, including the hourly historical PM2.5 concentrations, the daily mean meteorological data, and the annual nighttime light and normalized difference vegetation index (NDVI). The results showed that the proposed CR-LSTM model achieved better performance (coefficient of determination (R2) = 0.74; root mean square error (RMSE) = 18.96 μg/m3) than other common models, such as multiple linear regression (MLR), support vector regression (SVR), the conventional LSTM model, the LSTM extended (LSTME) model, and the temporal sliding LSTM extended (TS-LSTME) model. The proposed CR-LSTM model, implementing a combination of geographical rules, recursive strategy, and deep learning, shows improved performance in longer-term prediction tasks.

Similarities and Differences in the Temporal Variability of PM2.5 and AOD Between Urban and Rural Stations in Beijing

Surface particulate matter with an aerodynamic diameter of <2.5 μm (PM2.5) and column-integrated aerosol optical depth (AOD) exhibits substantial diurnal, daily, and yearly variabilities that are regionally dependent. The diversity of these temporal variabilities in urban and rural areas may imply the inherent mechanisms. A novel time-series analysis tool developed by Facebook, Prophet, is used to investigate the holiday, seasonal, and inter-annual patterns of PM2.5 and AOD at a rural station (RU) and an urban station (UR) in Beijing. PM2.5 shows a coherent decreasing tendency at both stations during 2014–2018, consistent with the implementation of the air pollution action plan at the end of 2013. RU is characterized by similar seasonal variations of AOD and PM2.5, with the lowest values in winter and the highest in summer, which is opposite that at UR with maximum AOD, but minimum PM2.5 in summer and minimum AOD, but maximum PM2.5 in winter. During the National Day holiday (1–7 October), both AOD and PM2.5 holiday components regularly shift from negative to positive departures, and the turning point generally occurs on October 4. AODs at both stations steadily increase throughout the daytime, which is most striking in winter. A morning rush hour peak of PM2.5 (7:00–9:00 local standard time (LST)) and a second peak at night (23:00 LST) are observed at UR. PM2.5 at RU often reaches minima (maxima) at around 12:00 LST (19:00 LST), about four hours later (earlier) than UR. The ratio of PM2.5 to AOD (η) shows a decreasing tendency at both stations in the last four years, indicating a profound impact of the air quality control program. η at RU always begins to increase about 1–2 h earlier than that at UR during the daytime. Large spatial and temporal variations of η suggest that caution should be observed in the estimation of PM2.5 from AOD.

Time series modeling of PM2.5 concentrations with residual variance constraint in eastern mainland China during 2013-2017

Satellite-based mapping has been proven to be an effective method to reveal the spatiotemporal variations of PM_2.5 distributions. However, most satellite AOD (aerosol optical depth) statistical models suffer from unstable accuracy over long time spans. This study thus aims to propose an accurate and stable method for PM_2.5 concentration estimations in time series. Specifically, a three-step residual variance constraint method (RVCM) is developed to simulate PM_2.5 concentrations from January 2013 to December 2017 with the aid of AODs and other auxiliary data. Results show that the five-year fitting R² and cross-validation R² of RVCMs improved from 0.77 to 0.88 and 0.71 to 0.84, respectively, compared to those models without residual variance constraint (WO-RVCM). Additionally, RVCM demonstrated more stable performance on time series simulation of PM_2.5 concentrations than WO-RVCM, with the yearly fitting R² of 0.89, 0.88, 0.85, 0.87 and 0.88, and corresponding cross validation R² of 0.85, 0.84, 0.80, 0.82 and 0.83, respectively. Furthermore, accuracy verification of removed outliers in residual variance constraint modeling confirmed the credibility of RVCM in outliers' simulation compared to WO-RVCM models. Finally, RVCM-aided estimations of time series PM_2.5 concentrations and associated premature deaths in the study area (east and southeast mainland China) revealed their total decrease rates were 35.21% and 21.57%, and excellent air quality days increased from 7% to 35%. These findings suggest that residual variance constraint is effective and could be a reliable solution to providing time series AOD-PM_2.5 modeling with stable accuracy over long time spans.

Assessment of German population exposure levels to PM10 based on multiple spatial-temporal data

Particulate matter is the key to increasing urban air pollution, and research into pollution exposure assessment is an important part of environmental health. In order to classify PM₁₀ air pollution and to investigate the population exposure to the distribution of PM₁₀, daily and monthly PM₁₀ concentrations of 379 air pollution monitoring stations were obtained for a period from 01/01/2017 to 31/12/2017. Firstly, PM₁₀ concentrations were classified using the head/tail break clustering algorithm to identify locations with elevated PM₁₀ levels. Subsequently, population exposure levels were calculated using population-weighted PM₁₀ concentrations. Finally, the power-law distribution was used to test the distribution of PM₁₀ polluted areas. Our results indicate that the head/tail break algorithm, with an appropriate segmentation threshold, can effectively identify areas with high PM₁₀ concentrations. The distribution of the population according to exposure level shows that the majority of people is living in polluted areas. The distribution of heavily PM₁₀ polluted areas in Germany follows the power-law distribution well, but their boundaries differ from the boundaries of administrative cities; some even cross several administrative cities. These classification results can guide policymakers in dividing the country into several areas for pollution control.

Estimation of PM2.5 Concentrations in China Using a Spatial Back Propagation Neural Network

Methods for estimating the spatial distribution of PM_2.5 concentrations have been developed but have not yet been able to effectively include spatial correlation. We report on the development of a spatial back-propagation neural network (S-BPNN) model designed specifically to make such correlations implicit by incorporating a spatial lag variable (SLV) as a virtual input variable. The S-BPNN fits the nonlinear relationship between ground-based air quality monitoring station measurements of PM_2.5, satellite observations of aerosol optical depth, meteorological synoptic conditions data and emissions data that include auxiliary geographical parameters such as land use, normalized difference vegetation index, elevation, and population density. We trained and validated the S-BPNN for both yearly and seasonal mean PM_2.5 concentrations. In addition, principal components analysis was employed to reduce the dimensionality of the data and a grid of neural network models was run to optimize the model design. The S-BPNN was cross-validated against an analogous but SLV-free BPNN model using the coefficient of determination (R²) and root mean squared error (RMSE) as statistical measures of goodness of fit. The inclusion of the SLV led to demonstrably superior performance of the S-BPNN over the BPNN with R² values increasing from 0.80 to 0.89 and with the RMSE decreasing from 8.1 to 5.8 μg/m³. The yearly mean PM_2.5 concentration in China during the study period was found to be 41.8 μg/m³ and the model estimated spatial distribution was found to exceed Level 2 of the China Ambient Air Quality Standards (CAAQS) enacted in 2012 (>35 μg/m³) in more than 70% of the Chinese territory. The inclusion of spatial correlation upgrades the performance of conventional BPNN models and provides a more accurate estimation of PM_2.5 concentrations for air quality monitoring.

Atmospheric heavy metal deposition in agro-ecosystems in China

Atmospheric deposition has become one of the main sources of heavy metals in crops in developed and industrial zones in China for the past several years. However, lack of data of the agro-ecosystems on the vast areas of China makes it difficult to assess the impacts of air pollution on the heavy metal accumulation in crops. In this study, with deposit samples from 67 sites located at different agro-ecosystems (typical, factory nearby, town nearby, roadside, and remote) of four natural regions [Huanghuai (HH), Southeast (SE), Southwest (SW) and upper-mid Yangzi River (Up-mid YR)], atmospheric heavy metal deposition in agro-ecosystems on a large scale in China was studied. The results showed that during the growing season, the deposition fluxes of Cr, Ni, As, Cd, and Pb in typical agro-ecosystems were 0.60-36.86, 0.65-25.37, 0.05-8.88, 0.12-5.81, and 0.43-35.63 μg m^-2 day^-1, respectively, which varied greatly between the four different regions. The average deposition fluxes of Cr, Ni, Cd, and Pb in the HH region, as well as the fluxes of As in the SW region, were significantly higher than those in the SE region. Heavy metal deposition rates among agro-ecosystems were very similar, except for the sites around cement factory in flat HH region. In mountainous SW region, however, deposition rates varied widely with sites nearby towns relatively higher and remote regions much lower. Higher correlation coefficients were observed between Cr, As, Pb, and Ni deposition rates, suggesting that they had similar sources. Samples from the SW and SE regions exhibited higher ²⁰⁷Pb/²⁰⁶Pb and ²⁰⁸Pb/²⁰⁶Pb ratios than those from the HH and Up-mid YR regions. Airborne Pb in SW agro-ecosystems were mainly derived from vehicle exhaust and local smelting, whereas that in the HH region from burning of northern Chinese coal.