Urban Air Quality Modelling

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 10858

Special Issue Editor

School of Geography, Earth and Environmental Sciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK
Interests: air quality modelling; clean air policies; large eddy simulation; street canyon; urban boundary layer processes; GIS

Special Issue Information

Dear Colleagues,

Urban air pollution has become the leading-order environmental risk for human health. As estimated by the World Health Organization (WHO), there are about 4.2 million annual premature deaths attributed to ambient air pollution. The WHO has updated its Air Quality Guidelines in September 2021, reflecting the fact that even exposure to lower levels of air pollutant can affect human health. It is important to better understand sources and processes of air pollutants and to develop effective clean air policies to reduce air pollution levels in the atmosphere. 

High-resolution air quality modeling can simulate combined effects of emission sources, chemical and physical processes. As air quality modeling has predictive capability, it can be used to develop effective policies for clean air in urban environments.

We call for papers on the modeling of physicochemical processes, improved understanding of air quality dispersion, source apportionment, quantification of the impacts of air pollution control policies (or co-benefits of Net Zero policies) on air pollution levels, at a variety of scales ranging from street canyon to neighborhood and city scales.

Dr. Jian Zhong
Guest Editor

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Keywords

  • air quality modeling
  • emission sources
  • physicochemical processes
  • dispersion
  • clean air policies
  • net zero policies
  • street canyon
  • urban environments

Published Papers (8 papers)

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Research

20 pages, 5323 KiB  
Article
Forecasting the Exceedances of PM2.5 in an Urban Area
by Stavros-Andreas Logothetis, Georgios Kosmopoulos, Orestis Panagopoulos, Vasileios Salamalikis and Andreas Kazantzidis
Atmosphere 2024, 15(5), 594; https://doi.org/10.3390/atmos15050594 - 13 May 2024
Viewed by 364
Abstract
Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, [...] Read more.
Particular matter (PM) constitutes one of the major air pollutants. Human exposure to fine PM (PM with a median diameter less than or equal to 2.5 μm, PM2.5) has many negative and diverse outcomes for human health, such as respiratory mortality, lung cancer, etc. Accurate air-quality forecasting on a regional scale enables local agencies to design and apply appropriate policies (e.g., meet specific emissions limitations) to tackle the problem of air pollution. Under this framework, low-cost sensors have recently emerged as a valuable tool, facilitating the spatiotemporal monitoring of air pollution on a local scale. In this study, we present a deep learning approach (long short-term memory, LSTM) to forecast the intra-day air pollution exceedances across urban and suburban areas. The PM2.5 data used in this study were collected from 12 well-calibrated low-cost sensors (Purple Air) located in the greater area of the Municipality of Thermi in Thessaloniki, Greece. The LSTM-based methodology implements PM2.5 data as well as auxiliary data, meteorological variables from the Copernicus Atmosphere Monitoring Service (CAMS), which is operated by ECMWF, and time variables related to local emissions to enhance the air pollution forecasting performance. The accuracy of the model forecasts reported adequate results, revealing a correlation coefficient between the measured PM2.5 and the LSTM forecast data ranging between 0.67 and 0.94 for all time horizons, with a decreasing trend as the time horizon increases. Regarding air pollution exceedances, the LSTM forecasting system can correctly capture more than 70.0% of the air pollution exceedance events in the study region. The latter findings highlight the model’s capabilities to correctly detect possible WHO threshold exceedances and provide valuable information regarding local air quality. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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22 pages, 1964 KiB  
Article
Spatiotemporal Distribution Characteristics and Inventory Analysis of Near-Road Traffic Pollution in Urban Areas
by Bingbing Li, Jiaren Li, Jiang Lu and Zhenyi Xu
Atmosphere 2024, 15(4), 417; https://doi.org/10.3390/atmos15040417 - 27 Mar 2024
Viewed by 736
Abstract
Vehicle emissions belong to the category of near-surface sources, occur close to human activity areas, and pose a greater threat to human health than other anthropogenic pollution sources. Furthermore, the study of the spatiotemporal characteristics of near-road traffic pollution is of great significance [...] Read more.
Vehicle emissions belong to the category of near-surface sources, occur close to human activity areas, and pose a greater threat to human health than other anthropogenic pollution sources. Furthermore, the study of the spatiotemporal characteristics of near-road traffic pollution is of great significance to urban and regional ambient air quality management, and is also an important basis for vehicle emission inventories, as well as the assessment of ambient air impact. Most previous studies have analyzed the spatiotemporal characteristics of hydrocarbons (HCs), carbon monoxide (CO), nitrogen oxides (NOx), and carbon dioxide (CO2) in urban vehicle emissions over a certain time, without considering the synergistic effect of mobile source particulate matter, NOx, and volatile organic compounds (VOCs). In this study, we analyze the composition of vehicles with different emission standards from road mobile sources in Anqing City, China. National category III and IV vehicles are the main contribution sources of various pollutants, accounting for more than 60% of emissions. Although national category I and II vehicles accounted for less than 1% of the total number of vehicles, their contribution to emissions cannot be ignored, especially for CO and HCs, the contribution of which from such vehicles can reach about 7%. This is mainly due to the low level of pollution control arising from the larger emission factor and greater age of these vehicles. Furthermore, eliminating old cars and increasing the proportion of national category VI vehicles can effectively reduce vehicle pollutant emissions. In terms of the spatiotemporal distribution characteristics, highways around urban areas are also the main sources of heavy vehicles, and the emission intensity of these pollutants is also higher on national roads and highways surrounding urban areas. In addition, the presence of m/p-xylene and toluene solvent-using species is detected, which indicates that petrol vehicle emissions, LPG and petrol volatilization, and solvent-using sources contribute significantly to ozone formation in the ozone pollution process. Comparing weekdays and non-weekdays, the PM2.5 peaks on non-weekdays are significantly higher than those on weekdays, and there is no “weekend effect”, which indicates that traffic emissions have little influence on PM2.5 emissions, and may be related to energy use and industrial pollution. Overall, this study strengthens the understanding of the relationship between emissions, traffic volumes, and vehicle types on spatial and temporal scales, and emphasizes the need for further investigation and comprehensive measures to mitigate pollution from these emissions. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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14 pages, 7647 KiB  
Article
Effects of Diesel Emissions on Black Carbon and Particle Number Concentrations in the Eastern U.S.
by Laura N. Posner and Spyros N. Pandis
Atmosphere 2024, 15(2), 199; https://doi.org/10.3390/atmos15020199 - 5 Feb 2024
Viewed by 806
Abstract
The effects of emissions of diesel engines on black carbon and particle number concentrations, as well as climate-relevant aerosol properties, are explored for a summertime period in the Eastern U.S. using the chemical transport model PMCAMx-UF. A 50% reduction in diesel particulate emissions [...] Read more.
The effects of emissions of diesel engines on black carbon and particle number concentrations, as well as climate-relevant aerosol properties, are explored for a summertime period in the Eastern U.S. using the chemical transport model PMCAMx-UF. A 50% reduction in diesel particulate emissions results in lower (23%) black carbon mass concentrations, as expected, and similar changes both in magnitude (27–30%) and spatial pattern for the absorption coefficient. However, an average 2% increase in the total particle number concentrations is predicted due to a decrease in the coagulation and condensation sinks and, at the same time, a 2% decrease in N100 (particles larger than 100 nm) concentrations. The diesel reduction results suggest that mitigation of large diesel particles and/or particle mass emissions can reduce climate-relevant properties related to the absorption of black carbon and provide health benefits; however, the changes could also have the unintended effect of increased ultrafine particle number concentrations. Changes in cloud condensation nuclei are predicted to be significantly less than expected, assuming a proportional reduction during this photochemically active period. Doubling the diesel emissions results in a domain-averaged 3% decrease in total particle number concentrations and a 3% increase in N100 concentrations. PM2.5 BC concentrations increase on average by 46%, and similar changes (52–60%) are predicted for the absorption coefficient. Extinction coefficients for both perturbation simulations changed by only a few percent due to the dominance of scattering aerosols in the Eastern U.S. during this period characterized by high photochemical activity. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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16 pages, 2950 KiB  
Article
Prediction of the Concentration and Source Contributions of PM2.5 and Gas-Phase Pollutants in an Urban Area with the SmartAQ Forecasting System
by Evangelia Siouti, Ksakousti Skyllakou, Ioannis Kioutsioukis, David Patoulias, Ioannis D. Apostolopoulos, George Fouskas and Spyros N. Pandis
Atmosphere 2024, 15(1), 8; https://doi.org/10.3390/atmos15010008 - 21 Dec 2023
Cited by 1 | Viewed by 825
Abstract
The SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km2) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban [...] Read more.
The SmartAQ (Smart Air Quality) forecasting system produces high-resolution (1 × 1 km2) air quality predictions in an urban area for the next three days using advanced chemical transport modeling. In this study, we evaluated the SmartAQ performance for the urban area of Patras, Greece, for four months (July 2021, September 2021, December 2021, and March 2022), covering all seasons. In this work, we assess the system’s ability to forecast PM2.5 levels and the major gas-phase pollutants during periods with different meteorological conditions and local emissions, but also in areas of the city with different characteristics (urban, suburban, and background sites). We take advantage of this SmartAQ application to also quantify the main sources of the pollutants at each site. During the summertime, PM2.5 model performance was excellent (Fbias < 15%, Ferror < 30%) for all sites both in the city center and suburbs. For the city center, the model reproduced well (MB = −0.9 μg m−3, ME = 2.5 μg m−3) the overall measured PM2.5 behavior and the high nighttime peaks due to cooking activity, as well as the transported PM pollution in the suburbs. During the fall, the SmartAQ PM2.5 performance was good (Fbias < 42%, Ferror < 45%) for the city center and the suburban core, while it was average (Fbias < 50%, Ferror < 54%, MB, ME < 3.3 μg m−3) for the suburbs because the model overpredicted the long-range transport of pollution. For wintertime, the system reproduced well (MB = −2 μg m−3, ME = 6.5 μg m−3) the PM2.5 concentration in the high-biomass-burning emission area with an excellent model performance (Fbias = −4%, Ferror = 33%) and reproduced well (MB < 1.1 μg m−3, ME < 3 μg m−3) the background PM2.5 levels. SmartAQ reproduced well the PM2.5 concentrations in the urban and suburban core during the spring (Fbias < 40%, Ferror < 50%, MB < 8.5 μg m−3, ME < 10 μg m−3), while it tended to slightly overestimate the regional pollution. The main local source of fine PM during summer and autumn was cooking, but most of the PM was transported to the city. Residential biomass burning was the dominant particle source of pollution during winter and early spring. For gas-phase pollutants, the system reproduced well the daily nitrogen oxides (NOx) concentrations during the summertime. Predicted NOx concentrations during the winter were consistent with measurements at night but underestimated the observations during the rest of the day. SmartAQ achieved the US EPA modeling goals for hourly O3 concentrations indicating good model performance. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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20 pages, 2077 KiB  
Article
Modelling of Deep Street Canyon Air Pollution Chemistry and Transport: A Wintertime Naples Case Study
by Yuqing Dai, Andrea Mazzeo, Jian Zhong, Xiaoming Cai, Benedetto Mele, Domenico Toscano, Fabio Murena and A. Rob MacKenzie
Atmosphere 2023, 14(9), 1385; https://doi.org/10.3390/atmos14091385 - 1 Sep 2023
Cited by 1 | Viewed by 1002
Abstract
The impact of urban morphology on air quality, particularly within deep canyons with longer residence times for complex chemical processes, remains insufficiently addressed. A flexible multi-box framework was used to simulate air quality at different canyon heights (3 m and 12 m). This [...] Read more.
The impact of urban morphology on air quality, particularly within deep canyons with longer residence times for complex chemical processes, remains insufficiently addressed. A flexible multi-box framework was used to simulate air quality at different canyon heights (3 m and 12 m). This approach incorporated essential parameters, including ventilation rates, background concentrations, photochemical schemes, and reaction coefficients. A field campaign within a deep canyon with an aspect ratio of 3.7, in Naples, Italy was conducted and used for the model evaluation. The model performance demonstrated good agreement, especially at the street level, when employing a realistic light intensity profile and incorporating volatile organic compound (VOC) chemistry. Our findings indicate that peroxyl radical production affects NO2 and O3 levels by up to 9.5% in deep canyons and underscore the significance of vertical distribution (approximately 5% variance) in health assessments and urban air quality strategy development. The model response was sensitive to changes in emissions as expected, but also, somewhat more surprisingly, to background conditions, emphasizing that policies to remove pollution hotspots must include local and broader citywide action. This work advances the understanding of air quality dynamics in deep urban canyons and presents a valuable tool for effective air quality management in intricate urban environments. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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19 pages, 14151 KiB  
Article
Improving Air Pollution Modelling in Complex Terrain with a Coupled WRF–LOTOS–EUROS Approach: A Case Study in Aburrá Valley, Colombia
by Jhon E. Hinestroza-Ramirez, Santiago Lopez-Restrepo, Andrés Yarce Botero, Arjo Segers, Angela M. Rendon-Perez, Santiago Isaza-Cadavid, Arnold Heemink and Olga Lucia Quintero
Atmosphere 2023, 14(4), 738; https://doi.org/10.3390/atmos14040738 - 19 Apr 2023
Cited by 4 | Viewed by 1469
Abstract
Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed [...] Read more.
Chemical transport models (CTM) are crucial for simulating the distribution of air pollutants, such as particulate matter, and evaluating their impact on the environment and human health. However, these models rely heavily on accurate emission inventory and meteorological inputs, usually obtained from reanalyzed weather data, such as the European Centre for Medium-Range Weather Forecasts (ECMWF). These inputs do not accurately reflect the complex topography and micro-scale meteorology in tropical regions where air pollution can pose a severe public health threat. We propose coupling the LOTOS–EUROS CTM model and the weather research and forecasting (WRF) model to improve LOTOS–EUROS representation. Using WRF as a meteorological driver provides high-resolution inputs for accurate pollutant simulation. We compared LOTOS–EUROS results when WRF and ECMWF provided the meteorological inputs during low and high pollutant concentration periods. The findings indicate that the WRF–LOTOS–EUROS coupling offers a more precise representation of the meteorology and pollutant dispersion than the default input of ECMWF. The simulations also capture the spatio-temporal variability of pollutant concentration and emphasize the importance of accounting for micro-scale meteorology and topography in air pollution modelling. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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18 pages, 11369 KiB  
Article
Air Quality Impact Estimation Due to Uncontrolled Emissions from Capuava Petrochemical Complex in the Metropolitan Area of São Paulo (MASP), Brazil
by Monique Silva Coelho, Daniel Constantino Zacharias, Tayná Silva de Paulo, Rita Yuri Ynoue and Adalgiza Fornaro
Atmosphere 2023, 14(3), 577; https://doi.org/10.3390/atmos14030577 - 17 Mar 2023
Cited by 1 | Viewed by 1634
Abstract
In the second quarter of 2021, the companies at the Capuava Petrochemical Complex (CPC, Santo André, Brazil) carried out a 50-day scheduled shutdown for the maintenance and installation of new industrial equipment. This process resulted in severe uncontrolled emissions of particulate matter (PM) [...] Read more.
In the second quarter of 2021, the companies at the Capuava Petrochemical Complex (CPC, Santo André, Brazil) carried out a 50-day scheduled shutdown for the maintenance and installation of new industrial equipment. This process resulted in severe uncontrolled emissions of particulate matter (PM) and volatile organic compounds (VOCs) in a densely populated residential area (~3400 inhabitants/km2). VOCs can be emitted directly into the atmosphere in urban areas by vehicle exhausts, fuel evaporation, solvent use, emissions of natural gas, and industrial processes. PM is emitted by vehicle exhausts, mainly those powered by diesel, industrial processes, and re-suspended soil dust, in addition to that produced in the atmosphere by photochemical reactions. Our statistical analyses compared the previous (2017–2020) and subsequent (2021–2022) periods from this episode (April–May 2021) from the official air quality monitoring network of the PM10, benzene, and toluene hourly data to improve the proportion of this period of uncontrolled emissions. Near-field simulations were also performed to evaluate the dispersion of pollutants of industrial origin, applying the Gaussian plume model AERMOD (steady-state plume model), estimating the concentrations of VOC and particulate matter (PM10) in which the population was exposed in the region surrounding the CPC. The results comparing the four previous years showed an increase in the mean concentrations by a factor of 2 for PM10, benzene, and toluene, reaching maximum values during the episode of 174 µg m−3 (PM10), 79.1 µg m−3 (benzene), and 58.7 µg m−3 (toluene). Meanwhile, these higher concentrations continued to be observed after the episode, but their variation cannot be fully explained yet. However, it is worth highlighting that this corresponds to the post-pandemic period and the 2022 data also correspond to the period from January to June, that is, they do not represent the annual variation. A linear correlation indicated that CPC could have been responsible for more than 60% of benzene measured at the Capuava Air Quality Station (AQS). However, the PM10 behavior was not fully explained by the model. AERMOD showed that the VOC plume had the potential to reach a large part of Mauá and Santo André municipalities, with the potential to affect the health of more than 1 million inhabitants. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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24 pages, 3449 KiB  
Article
Characteristics of PM10 Level during Haze Events in Malaysia Based on Quantile Regression Method
by Siti Nadhirah Redzuan, Norazian Mohamed Noor, Nur Alis Addiena A. Rahim, Izzati Amani Mohd Jafri, Syaza Ezzati Baidrulhisham, Ahmad Zia Ul-Saufie, Andrei Victor Sandu, Petrica Vizureanu, Mohd Remy Rozainy Mohd Arif Zainol and György Deák
Atmosphere 2023, 14(2), 407; https://doi.org/10.3390/atmos14020407 - 20 Feb 2023
Cited by 1 | Viewed by 2404
Abstract
Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting [...] Read more.
Malaysia has been facing transboundary haze events repeatedly, in which the air contains extremely high particulate matter, particularly PM10, which affects human health and the environment. Therefore, it is crucial to understand the characteristics of PM10 concentration and develop a reliable PM10 forecasting model for early information and warning alerts to the responsible parties in order for them to mitigate and plan precautionary measures during such events. This study aims to analyze PM10 variation and investigate the performance of quantile regression in predicting the next-day, the next two days, and the next three days of PM10 levels during a high particulate event. Hourly secondary data of trace gases and the weather parameters at Pasir Gudang, Melaka, and Petaling Jaya during historical haze events in 1997, 2005, 2013, and 2015. The Pearson correlation was calculated to find the correlation between PM10 level and other parameters. Moderate correlated parameters (r > 0.3) with PM10 concentration were used to develop a Pearson–QR model with percentiles of 0.25, 0.50, and 0.75 and were compared using quantile regression (QR) and multiple linear regression (MLR). Several performance indicators, namely mean absolute error (MAE), root mean squared error (RMSE), coefficient of determination (R2), and index of agreement (IA), were calculated to evaluate and compare the performances of the predictive model. The highest daily average of PM10 concentration was monitored in Melaka within the range of 69.7 and 83.3 µg/m3. CO and temperature were the most significant parameters associated with PM10 level during haze conditions. Quantile regression at p = 0.75 shows high efficiency in predicting PM10 level during haze events, especially for the short-term prediction in Melaka and Petaling Jaya, with an R2 value of >0.85. Thus, the QR model has high potential to be developed as an effective method for forecasting air pollutant levels, especially during unusual atmospheric conditions when the overall mean of the air pollutant level is not suitable for use as a model. Full article
(This article belongs to the Special Issue Urban Air Quality Modelling)
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