ABSTRACT
The analysis of the daily spatial patterns of near-surface Nitrogen dioxide (NO2) concentrations can assist decision makers mitigate this common air pollutant in urban areas. However, comparative analysis of NO2 estimates in different urban agglomerations of China is limited. In this study, a new linear mixed effect model (LME) with multi-source spatiotemporal data is proposed to estimate daily NO2 concentrations at high accuracy based on the land-use regression (LUR) model and Ozone Monitoring Instrument (OMI) and TROPOspheric Monitoring Instrument (TROPOMI) products. In addition, three models for NO2 concentration estimation were evaluated and compared in four Chinese urban agglomerations from 2018 to 2020, including the COVID-19 closed management period. Each model included a unique combination of methods and satellite NO2 products: ModelⅠ: LUR model with OMI products;Model Ⅱ: LUR model with TropOMI products;Model Ⅱ: LME model with TropOMI products. The results show that the LME model outperformed the LUR model in all four urban agglomerations as the average RMSE decreased by 16.09% due to the consideration of atmospheric dispersion random effects, and using TropOMI instead of OMI products can improve the accuracy. Based on our NO2 estimations, pollution hotspots were identified, and pollution anomalies during the COVID-19 period were explored for two periods;the lockdown and revenge pollution periods. The largest NO2 pollution difference between the hotspot and non-hotspot areas occurred in the second period, especially in the heavy industrial urban agglomerations. © 2022 Elsevier Ltd
ABSTRACT
The transport sector has been identified as one of the main contributors to nitrogen dioxide (NO2) pollution in Ireland. This research develops an enhanced Wind Sector Land Use Regression (WS-LUR) model to estimate NO2 concentrations across Ireland, in areas where air pollution monitoring is not available. The model incorporates details of the vehicle fleet breakdown to weight vehicle type flows based on the emission rates of the vehicle type, differentiating routes with varying proportions of heavier emitting vehicles. In 2008, car taxation underwent a significant change from an engine size based system to a carbon dioxide (CO2) emission rate based system resulting in a significant transition towards diesel fuelled vehicles. A mitigation strategy to remove diesel fuelled vehicles from the public service vehicle fleet was tested achieving predicted NO2 reductions in the range of 0.3 μg/m3 to 1.9 μg/m3. The impact of COVID-19 on NO2 concentration levels was also investigated. © 2022 Elsevier Ltd
ABSTRACT
High levels of ground level ozone (O3) are associated with detrimental health concerns. Most of the studies only focused on daily average and daytime trends due to the presence of sunlight that initiates its formation. However, atmospheric chemical reactions occur all day, thus, nighttime concentrations should be given equal importance. In this study, geospatial-artificial intelligence (Geo-AI) which combined kriging, land use regression (LUR), machine learning, an ensemble learning, was applied to develop ensemble mixed spatial models (EMSMs) for daily, daytime, and nighttime periods. These models were used to estimate the long-term O3 spatio-temporal variations using a two-decade worth of in-situ measurements, meteorological parameters, geospatial predictors, and social and season-dependent factors. From the traditional LUR approach, the performance of EMSMs improved by 60% (daytime), 49% (nighttime), and 57% (daily). The resulting daily, daytime, and nighttime EMSMs had a high explanatory power with and adjusted R2 of 0.91, 0.91, and 0.88, respectively. Estimation maps were produced to examine the changes before and during the implementation of nationwide COVID-19 restrictions. These results provide accurate estimates and its diurnal variation that will support pollution control measure and epidemiological studies.
Subject(s)
Air Pollutants , Air Pollution , COVID-19 , Ozone , Humans , Ozone/analysis , Air Pollutants/analysis , Artificial Intelligence , Taiwan , Environmental Monitoring/methods , Air Pollution/analysis , Particulate Matter/analysisABSTRACT
The transport sector has been identified as one of the main contributors to nitrogen dioxide (NO2) pollution in Ireland. This research develops an enhanced Wind Sector Land Use Regression (WS-LUR) model to estimate NO2 concentrations across Ireland, in areas where air pollution monitoring is not available. The model incorporates details of the vehicle fleet breakdown to weight vehicle type flows based on the emission rates of the vehicle type, differentiating routes with varying proportions of heavier emitting vehicles. In 2008, car taxation underwent a significant change from an engine size based system to a carbon dioxide (CO2) emission rate based system resulting in a significant transition towards diesel fuelled vehicles. A mitigation strategy to remove diesel fuelled vehicles from the public service vehicle fleet was tested achieving predicted NO2 reductions in the range of 0.3 μg/m3 to 1.9 μg/m3. The impact of COVID-19 on NO2 concentration levels was also investigated.
ABSTRACT
Nitrogen dioxide (NO2) is an important target for monitoring atmospheric quality. Deriving ground-level NO2 concentrations with much finer resolution, it requires high-resolution satellite tropospheric NO2 column as input and a reliable estimation algorithm. This paper aims to estimate the daily ground-level NO2 concentrations over China based on machine learning models and the TROPOMI NO2 data with high spatial resolution. In this study, four tree-based algorithm machine learning models, decision trees (DT), gradient boost decision tree (GBDT), random forest (RF) and extra-trees (ET), were used to estimate ground-level NO2 concentrations. In addition to considering many influencing factors of the ground-level NO2 concentrations, we especially introduced simplified temporal and spatial information into the estimation models. The results show that the extra-trees with spatial and temporal information (ST-ET) model has great performance in estimating ground-level NO2 concentrations with a cross-validation R-2 of 0.81 and RMSE of 3.45 mu g/m(3) in test datasets. The estimated results for 2019 based on the ST-ET model achieves a satisfactory accuracy with a cross-validation R-2 of 0.86 compared with the other models. Through time-space analysis and comparison, it was found that the estimated high-resolution results were consistent with the ground observed NO2 concentrations. Using data from January 2020 to test the prediction power of the models, the results indicate that the ST-ET model has a good performance in predicting ground-level NO2 concentrations. Taking four ground-level NO2 concentrations hotspots as examples, the estimated ground-level NO2 concentrations and ground-based observation data during the coronavirus disease (COVID-19) pandemic were lower compared with the same period in 2019. The findings offer a solid solution for accurately and efficiently estimating ground-level NO2 concentrations by using satellite observations, and provide useful information for improving our understanding of the regional atmospheric environment.
ABSTRACT
Using spatially- and temporally-resolved data from the New York City Community Air Survey (NYCCAS) and the New York State (NYS) Department of Environmental Conservation (DEC) network, we characterized changes in fine particulate matter (PM2.5) and nitrogen dioxide (NO2) following the COVID-19 shutdown in NYC (3/20/20 – 6/7/20). Difference-in-difference analysis of PM2.5 and NO2 measured at 93 sites were used to estimate the change in citywide pollution attributable to the shutdown. We also quantified how these pollutant changes varied among different demographic groups using difference-in-difference analyses stratified by neighborhood poverty levels and rates of PM2.5-attributable health outcomes. Spatial patterns of PM2.5 and NO2 were interpolated across NYC by fitting land-use regression models to measurements at the 93 sites. Weather conditions and emission source trends were analyzed to determine the potential effects of meteorology and specific emission sources on the observed pollution changes. We estimate that citywide average PM2.5 and NO2 decreased by approximately 25% and 29%, respectively, due to the shutdown. Weather readings show little evidence that meteorology biased our results in the direction of our findings. Data on major sources of PM2.5 and NO2 pollution suggests that decreased vehicle traffic and commercial cooking contributed to declines in air pollution during this period. Pollution reductions occurred disproportionately in the city's central business district (CBD), with smaller changes in other areas of the city, such as those with the highest burden of air pollution-related health impacts. These findings emphasize the need to target pollution sources in communities that suffer the greatest from pollution exposure in the design of equitable environmental health policy. © 2022
ABSTRACT
PM2.5 and PM10 could increase the risk for cardiovascular and respiratory diseases in the general public and severely limit the sustainable development in urban areas. Land use regression models are effective in predicting the spatial distribution of atmospheric pollutants, and have been widely used in many cities in Europe, North America and China. To reveal the spatial distribution characteristics of PM2.5 and PM10 in Xi'an during the heating seasons, the authors established two regression prediction models using PM2.5 and PM10 concentrations from 181 monitoring stations and 87 independent variables. The model results are as follows: for PM2.5, R2 = 0.713 and RMSE = 8.355 µg/m3; for PM10, R2 = 0.681 and RMSE = 14.842 µg/m3. In addition to the traditional independent variables such as area of green space and road length, the models also include the numbers of pollutant discharging enterprises, restaurants, and bus stations. The prediction results reveal the spatial distribution characteristics of PM2.5 and PM10 in the heating seasons of Xi'an. These results also indicate that the spatial distribution of pollutants is closely related to the layout of industrial land and the location of enterprises that generate air pollution emissions. Green space can mitigate pollution, and the contribution of traffic emission is less than that of industrial emission. To our knowledge, this study is the first to apply land use regression models to the Fenwei Plain, a heavily polluted area in China. It provides a scientific foundation for urban planning, land use regulation, air pollution control, and public health policy making. It also establishes a basic model for population exposure assessment, and promotes the sustainability of urban environments.