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Special Issue "Urban Air Quality Modelling"

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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 (2 papers)

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Research

Open AccessArticle

Air Quality Impact Estimation Due to Uncontrolled Emissions from Capuava Petrochemical Complex in the Metropolitan Area of São Paulo (MASP), Brazil

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

Viewed by 291

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) 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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Open AccessArticle

Characteristics of PM10 Level during Haze Events in Malaysia Based on Quantile Regression Method

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

Viewed by 840

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 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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