Atmosphere

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23 pages, 4330 KiB

Open AccessArticle

High-Resolution Modeling of Air Quality in Abidjan (Côte d’Ivoire) Using a New Urban-Scale Inventory

by Sylvain Gnamien, Cathy Liousse, Sekou Keita, Rajesh Kumar and Véronique Yoboué

Atmosphere 2024, 15(7), 758; https://doi.org/10.3390/atmos15070758 - 25 Jun 2024

Viewed by 1165

In West African cities, the impacts of the air quality on the health of the population is expected to increase significantly in the near future. For the first time to our knowledge, we conducted a high-resolution modeling study over Abidjan (Côte d’Ivoire) using [...] Read more.

In West African cities, the impacts of the air quality on the health of the population is expected to increase significantly in the near future. For the first time to our knowledge, we conducted a high-resolution modeling study over Abidjan (Côte d’Ivoire) using the WRF-Chem model and the simplified GOCART model to simulate carbonaceous aerosols BC and OC, sulfate, dust, sea salt, PM_2.5, and PM₁₀. The simulations were carried out during January and February 2019, a period over which there are databases of observations available. The DACCIWA inventory provided anthropogenic emissions at the regional scale, whereas a new emission inventory has been developed for the city of Abidjan. In 2019, the emissions were 4986.8 Gg for BC, 14,731.4 Gg for OC, and 7751.6 Gg for SO₂. Domestic fires were the primary OC source (7719.5 Gg), while road traffic was the largest BC emitter (2198.8 Gg). Our modeling results generally overestimate urban particle concentrations, despite having a better agreement for those based on the inventory of the city of Abidjan. Modeled concentrations of BC are higher in administrative centers due to road traffic, while OC concentrations are significant in densely populated neighborhoods. Full article

(This article belongs to the Special Issue Urban Air Quality Modelling)

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15 pages, 1192 KiB

Open AccessArticle

Spatial Analysis of Intra-Urban Air Pollution Disparities through an Environmental Justice Lens: A Case Study of Philadelphia, PA

by Madeline Scolio, Charlotte Borha, Peleg Kremer and Kabindra M. Shakya

Atmosphere 2024, 15(7), 755; https://doi.org/10.3390/atmos15070755 - 24 Jun 2024

Cited by 1 | Viewed by 2300

Abstract

Urban air pollution has been long understood as a critical threat to human health worldwide. Worsening urban air quality can cause increased rates of asthma, respiratory illnesses, and mortality. Air pollution is also an important environmental justice issue as it disproportionately burdens populations [...] Read more.

Urban air pollution has been long understood as a critical threat to human health worldwide. Worsening urban air quality can cause increased rates of asthma, respiratory illnesses, and mortality. Air pollution is also an important environmental justice issue as it disproportionately burdens populations made vulnerable by their socioeconomic and health status. Using spatially continuous fine-scale air quality data for the city of Philadelphia, this study analyzed the relationship between two air pollutants: particulate matter (PM_2.5, black carbon (BC), and three dimensions of vulnerability: social (non-White population), economic (poverty), and health outcomes (asthma prevalence). Spatial autoregressive models outperformed Ordinary Least Squares (OLS) regression, indicating the importance of considering spatial autocorrelation in air pollution-related environmental-justice modeling efforts. Positive relationships were observed between PM_2.5 concentrations and the socioeconomic variables and asthma prevalence. Percent non-White population was a significant predictor of BC for all models, while percent poverty was shown to not be a significant predictor of BC in the best fitting model. Our findings underscore the presence of distributive environmental injustices, where marginalized communities may bear a disproportionate burden of air pollution within Philadelphia. Full article

(This article belongs to the Special Issue Urban Air Quality Modelling)

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26 pages, 7611 KiB

Open AccessArticle

Numerical Analysis of the Effects of Different Window-Opening Strategies on the Indoor Pollutant Dispersion in Street-Facing Buildings

by Yongjia Wu, Yilian Ouyang, Tianhao Shi, Zhiyong Li and Tingzhen Ming

Atmosphere 2024, 15(6), 724; https://doi.org/10.3390/atmos15060724 - 17 Jun 2024

Viewed by 1178

Abstract

The idling of automobiles at street intersections can lead to pollutant accumulation which impacts the health of residents in street-facing buildings. Previous research focused on pollutant dispersion within street canyons and did not consider the coupling of indoor and outdoor pollutants. This paper [...] Read more.

The idling of automobiles at street intersections can lead to pollutant accumulation which impacts the health of residents in street-facing buildings. Previous research focused on pollutant dispersion within street canyons and did not consider the coupling of indoor and outdoor pollutants. This paper employs the computational fluid dynamics (CFD) method to simulate the dispersion characteristics of vehicle emission pollutants in street canyons, primarily investigating the indoor and outdoor pollutant dispersion patterns under various window opening configurations (single-sided ventilation, corner ventilation, and different positions of the glass under corner ventilation). Additionally, the study considers the impacts of the aspect ratio and ambient wind speed. Studies have shown that corner ventilation is effective in reducing indoor pollutant levels. When the two window glass positions are far away from the center of the intersection, the average CO mass fraction in the single-sided ventilation room is reduced by 87.1%. The average indoor CO mass fraction on the leeward side decreases with the increasing wind speed and aspect ratio. At a wind speed of 8 m/s, the average indoor CO mass fraction on the leeward side decreases to 2.45 × 10⁻⁸. At an aspect ratio of 2, the indoor CO mass fraction on the leeward side decreases with increasing floors before stabilizing at approximately 4.77 × 10⁻⁹. This study suggests optimal window opening strategies to reduce indoor pollutant levels in street-facing buildings at street intersections, offering guidance to indoor residents on window ventilation practices. Full article

(This article belongs to the Special Issue Urban Air Quality Modelling)

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20 pages, 5323 KiB

Open AccessArticle

Forecasting the Exceedances of PM_2.5 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

Cited by 4 | Viewed by 2173

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, PM_2.5) 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, PM_2.5) 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 PM_2.5 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 PM_2.5 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 PM_2.5 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

Open AccessArticle

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

Cited by 3 | Viewed by 1727

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 PM_2.5 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 PM_2.5 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

Open AccessEditor’s ChoiceArticle

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 1477

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. PM_2.5 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

Open AccessArticle

Prediction of the Concentration and Source Contributions of PM_2.5 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 3 | Viewed by 1455

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 PM_2.5 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, PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 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 PM_2.5 levels. SmartAQ reproduced well the PM_2.5 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 (NO_x) concentrations during the summertime. Predicted NO_x 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

Open AccessArticle

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 2 | Viewed by 2026

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

Open AccessArticle

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 5 | Viewed by 2401

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

Open AccessArticle

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 7 | Viewed by 2562

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

Open AccessArticle

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 3 | Viewed by 3760

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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