Air Pollution Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Atmospheric Techniques, Instruments, and Modeling".

Deadline for manuscript submissions: closed (1 July 2022) | Viewed by 6863

Special Issue Editors

Regional Atmospheric Environment Group, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
Interests: meteorological modeling; air quality modeling; land surface model; aerosol feedbacks; two-way coupled meteorology-air quality models
Laboratory of Marine Environmental Science and Ecology (MoE), Institute for Advanced Ocean Study, Ocean University of China, Qingdao 266100, China
Interests: tropospheric ozone; ozone-climate interactions; air pollution
Special Issues, Collections and Topics in MDPI journals
Regional Atmospheric Environment Group, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, China
Interests: air quality modeling; climate modeling; snow albedo modeling; desert dust; black carbon; health effects; satellite retrieval; data visualization

Special Issue Information

Dear Colleagues,

Deteriorating air quality is linked to many health problems that raise concerns among government agencies and the scientific community. To tackle air pollution problems and provide early warnings to the public, numerical air quality models have proved to be a very useful tool. For the past several decades, a number of air quality models have been developed, enhanced, improved, and applied. However, the accuracies of air quality simulations and forecasts are sometimes unsatisfactory, and air pollution problems in many parts of the world need to be further investigated with modelling. 

Through this Special Issue, we intend to provide a publication platform for researchers to exchange new ideas, findings, and challenges in air pollution modelling. Research and review papers are encouraged with regard to a variety of issues relevant to air pollution modelling, such as emission modelling, meteorology modelling related to air pollution, representations of physical and chemical processes in air quality models, air pollution modelling at multiple scales, model evaluations and comparisons, real-time air quality forecasting, two-way coupled meteorology and air quality models, the interactions between air quality and climate, as well as the impacts of air quality on public health.

Dr. Aijun Xiu
Dr. Yang Gao
Dr. Xuelei Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • air quality models
  • emission modelling
  • two-way coupled models
  • model evaluation
  • real-time forecasting
  • climate and air quality interactions

Published Papers (4 papers)

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Research

16 pages, 1676 KiB  
Article
A Coverage Sampling Path Planning Method Suitable for UAV 3D Space Atmospheric Environment Detection
Atmosphere 2022, 13(8), 1321; https://doi.org/10.3390/atmos13081321 - 19 Aug 2022
Cited by 3 | Viewed by 1427
Abstract
Air pollution affects people’s life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit [...] Read more.
Air pollution affects people’s life and health, and controlling air pollution requires the collection of polluting gas information. Unmanned aerial vehicles (UAVs) have been used for environmental detection due to their characteristics. However, the limitation of onboard energy sources of UAVs will limit the coverage of the detection area and the number of gas samples collected, which will affect the assessment of pollution levels. In addition, to truly and completely reflect the distribution of atmospheric pollutants, it is necessary to sample the entire three-dimensional space. This paper proposes a three-dimensional space path planning method suitable for UAV atmospheric environment detection, which can generate a full-coverage path with optimal coverage density under energy constraints. Simulation results show that the proposed method can effectively improve the coverage density compared with other path generation methods. Field experiments show that the proposed method is reliable and accurate in the application of UAV atmospheric environment space detection. Full article
(This article belongs to the Special Issue Air Pollution Modelling)
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18 pages, 5449 KiB  
Article
Modelling Hourly Particulate Matter (PM10) Concentrations at High Spatial Resolution in Germany Using Land Use Regression and Open Data
Atmosphere 2022, 13(8), 1282; https://doi.org/10.3390/atmos13081282 - 12 Aug 2022
Viewed by 1437
Abstract
Air pollution is a major health risk factor worldwide. Regular short- and long-time exposures to ambient particulate matter (PM) promote various diseases and can lead to premature death. Therefore, in Germany, air quality is assessed continuously at approximately 400 measurement sites. However, knowledge [...] Read more.
Air pollution is a major health risk factor worldwide. Regular short- and long-time exposures to ambient particulate matter (PM) promote various diseases and can lead to premature death. Therefore, in Germany, air quality is assessed continuously at approximately 400 measurement sites. However, knowledge about this intermediate distribution is either unknown or lacks a high spatial–temporal resolution to accurately determine exposure since commonly used chemical transport models are resource intensive. In this study, we present a method that can provide information about the ambient PM concentration for all of Germany at high spatial (100 m × 100 m) and hourly resolutions based on freely available data. To do so we adopted and optimised a method that combined land use regression modelling with a geostatistical interpolation technique using ordinary kriging. The land use regression model was set up based on CORINE (Coordination of Information on the Environment) land cover data and the Germany National Emission Inventory. To test the model’s performance under different conditions, four distinct data sets were used. (1) From a total of 8760 (365 × 24) available h, 1500 were randomly selected. From those, the hourly mean concentrations at all stations (ca. 400) were used to run the model (n = 566,326). The leave-one-out cross-validation resulted in a mean absolute error (MAE) of 7.68μgm3 and a root mean square error (RMSE) of 11.20μgm3. (2) For a more detailed analysis of how the model performs when an above-average number of high values are modelled, we selected all hourly means from February 2011 (n = 256,606). In February, measured concentrations were much higher than in any other month, leading to a slightly higher MAE of 9.77μgm3 and RMSE of 14.36μgm3, respectively. (3) To enable better comparability with other studies, the annual mean concentration (n = 413) was modelled with a MAE of 4.82μgm3 and a RMSE of 6.08μgm3. (4) To verify the model’s capability of predicting the exceedance of the daily mean limit value, daily means were modelled for all days in February (n = 10,845). The exceedances of the daily mean limit value of 50 μgm3 were predicted correctly in 88.67% of all cases. We show that modelling ambient PM concentrations can be performed at a high spatial–temporal resolution for large areas based on open data, land use regression modelling, and kriging, with overall convincing results. This approach offers new possibilities in the fields of exposure assessment, city planning, and governance since it allows more accurate views of ambient PM concentrations at the spatial–temporal resolution required for such assessments. Full article
(This article belongs to the Special Issue Air Pollution Modelling)
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14 pages, 4617 KiB  
Article
Effects of Evaporative Emissions Control Measurements on Ozone Concentrations in Brazil
Atmosphere 2022, 13(1), 82; https://doi.org/10.3390/atmos13010082 - 05 Jan 2022
Cited by 2 | Viewed by 1528
Abstract
In this work, the possible benefits obtained due to the implementation of evaporative emissions control measures, originating from vehicle fueling processes, on ozone concentrations are verified. The measures studied are: (1) control at the moment when the tank trucks supply the fuel to [...] Read more.
In this work, the possible benefits obtained due to the implementation of evaporative emissions control measures, originating from vehicle fueling processes, on ozone concentrations are verified. The measures studied are: (1) control at the moment when the tank trucks supply the fuel to the gas stations (Stage 1); (2) control at the moment when the vehicles are refueled at the gas stations, through a device installed in the pumps (Stage 2); (3) same as the previous control, but through a device installed in the vehicles (ORVR). The effects of these procedures were analyzed using numerical modeling with the VEIN and WRF/Chem models for a base case in 2018 and different emission scenarios, both in 2018 and 2031. The results obtained for 2018 show that the implementation of Stages 1 and 2 would reduce HCNM emissions by 47.96%, with a consequent reduction of 19.9% in the average concentrations of tropospheric ozone. For 2031, the greatest reductions in ozone concentrations were obtained with the scenario without ORVR, and with Stage 1 and Stage 2 (64.65% reduction in HCNM emissions and 31.93% in ozone), followed by the scenario with ORVR and with Stage 1 and Stage 2 (64.39% reduction in HCNM emissions and 32.98% in ozone concentrations). Full article
(This article belongs to the Special Issue Air Pollution Modelling)
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26 pages, 754 KiB  
Article
A Fast-Converging Kernel Density Estimator for Dispersion in Horizontally Homogeneous Meteorological Conditions
Atmosphere 2021, 12(10), 1343; https://doi.org/10.3390/atmos12101343 - 14 Oct 2021
Cited by 3 | Viewed by 1569
Abstract
Kernel smoothers are often used in Lagrangian particle dispersion simulations to estimate the concentration distribution of tracer gasses, pollutants etc. Their main disadvantage is that they suffer from the curse of dimensionality, i.e., they converge at a rate of [...] Read more.
Kernel smoothers are often used in Lagrangian particle dispersion simulations to estimate the concentration distribution of tracer gasses, pollutants etc. Their main disadvantage is that they suffer from the curse of dimensionality, i.e., they converge at a rate of 4/(d+4) with d the number of dimensions. Under the assumption of horizontally homogeneous meteorological conditions, we present a kernel density estimator that estimates a 3D concentration field with the faster convergence rate of a 1D kernel smoother, i.e., 4/5. This density estimator has been derived from the Langevin equation using path integral theory and simply consists of the product between a Gaussian kernel and a 1D kernel smoother. Its numerical convergence rate and efficiency are compared with that of a 3D kernel smoother. The convergence study shows that the path integral-based estimator has a superior convergence rate with efficiency, in mean integrated squared error sense, comparable with the one of the optimal 3D Epanechnikov kernel. Horizontally homogeneous meteorological conditions are often assumed in near-field range dispersion studies. Therefore, we illustrate the performance of our method by simulating experiments from the Project Prairie Grass data set. Full article
(This article belongs to the Special Issue Air Pollution Modelling)
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