The Assessment of Air Pollutant Concentrations and Air Quality Index in Shiraz during 2011-2013

The quality of air affects lives and the environment at large. Poor air quality has claimed many lives and distorted the environment across the globe, and much more severely in African countries where air quality monitoring systems are scarce or even do not exist. Here in Africa, dirty air is brought about by the growth in industrialization, urbanization, flights, and road traffic. Air pollution remains such a silent killer, especially in Africa, and if not dealt with, it will continue to lead to health issues, such as heart conditions, stroke, and chronic respiratory organ unwellness, which later result in death. In this paper, the Kampala Air Quality Index prediction model based on the fuzzy logic inference system was designed to determine the air quality for Kampala city, according to the air pollutant concentrations (nitrogen dioxide, sulphur dioxide and fine particulate matter 2.5). It is observed that fuzzy logic algorithms are capable of determining the air quality index and therefore, can be used to predict and estimate the air quality index in real time, based on the given air pollutant concentrations. Hence, this can reduce the effects of air pollution on both humans and the environment.

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Regional Energy, CO2, and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach

Energies ◽

10.3390/en11082119 ◽

2018 ◽

Vol 11 (8) ◽

pp. 2119 ◽

Cited By ~ 3

Author(s):

Ying Li ◽

Yung-Ho Chiu ◽

Liang Lu

Keyword(s):

Particulate Matter ◽

Energy Consumption ◽

Air Quality ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Significant Difference ◽

Desirable Output ◽

Reduce Emissions ◽

Carbon Dioxide Co2

Rapid economic development has resulted in a significant increase in energy consumption and pollution such as carbon dioxide (CO2), particulate matter (PM2.5), particulate matter 10 (PM10), SO2, and NO2 emissions, which can cause cardiovascular and respiratory diseases. Therefore, to ensure a sustainable future, it is essential to improve economic efficiency and reduce emissions. Using a Meta-frontier Non-radial Directional Distance Function model, this study took energy consumption, the labor force, and fixed asset investments as the inputs, Gross domestic product (GDP) as the desirable output, and CO2 and the Air Quality Index (AQI) scores as the undesirable outputs to assess energy efficiency and air pollutant index efficiency scores in China from 2013–2016 and to identify the areas in which improvements was necessary. It was found that there was a large gap between the western and eastern cities in China. A comparison of the CO2 and AQI in 31 Chinese cities showed a significant difference in the CO2 emissions and AQI efficiency scores, with the lower scoring cities being mainly concentrated in China’s western region. It was therefore concluded that China needs to pay greater attention to the differences in the economic levels, stages of social development, and energy structures in the western cities when developing appropriately focused improvement plans.

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La surveillance de la qualité de l'air dans les Hauts-de-France

La Météorologie ◽

10.37053/lameteorologie-2021-0092 ◽

2021 ◽

pp. 045

Author(s):

Jimmy Leyes ◽

Laure Roussel

Keyword(s):

Air Quality ◽

Air Pollutant ◽

Quality Monitoring ◽

Air Quality Monitoring ◽

Non Profit ◽

The Public ◽

Pollutant Concentrations ◽

Ambient Concentrations ◽

Air Pollutant Concentrations ◽

Qualité De L’Air

La surveillance réglementaire de la qualité de l'air en France est confiée aux associations régionales agréées de surveillance de la qualité de l'air (Aasqa) telles qu'Atmo Hauts-de-France. Elles s'appuient sur une palette d'outils et leur expertise pour mesurer les polluants dans l'air de leur territoire, alerter les populations en cas d'épisode de pollution, répondre aux exigences réglementaires de surveillance définies au niveau européen, tout en prenant en compte les spécificités régionales. Cet article présente les différents outils utilisés par les Aasqa, et plus particulièrement Atmo Hauts-de-France, pour surveiller et estimer la qualité de l'air. L'association régionale opère ainsi un ensemble de stations de mesures fixes et mobiles pour suivre en continu les concentrations de polluants réglementés ou non sur son territoire, et dispose d'outils de modélisation pour évaluer et prévoir la qualité de l'air en tous points de la région. Cet article présente également certains des paramètres météorologiques qui influencent la qualité de l'air de la région Hauts-de-France, particulièrement concernée par les épisodes de pollution aux particules. Regulatory air quality monitoring in France is performed by government-approved non-profit organisations called AASQAs, one of which is Atmo Hauts-de-France. These organisations rely on decades of accumulated air quality expertise and use several techniques to measure air pollutant concentrations, inform the public when pollutant levels are unhealthy, and comply with E.U. air quality monitoring regulations. This paper gives an overview of the tools used by AASQAs, and more particularly by Atmo Hauts-de-France, to monitor and forecast air quality. The year-round continuous monitoring of air pollutant levels at fixed sites is supplemented by short-term measurements made with fully-equipped vehicles or trailers and by modelling tools that forecast air quality and estimate pollutant levels where there are no measurements. AASQAs study pollutants which ambient concentrations are regulated by European air quality standards as well as other pollutants which are not regulated in this way. This work also discusses some of the meteorological factors, that affect air quality in the region Hauts-de-France, which is heavily impacted by particulate matter pollution.

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Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Valley, Nepal

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-8209-2019 ◽

2019 ◽

Vol 19 (12) ◽

pp. 8209-8228 ◽

Cited By ~ 5

Author(s):

Min Zhong ◽

Eri Saikawa ◽

Alexander Avramov ◽

Chen Chen ◽

Boya Sun ◽

...

Keyword(s):

Air Quality ◽

Vehicle Emissions ◽

Air Pollutant ◽

Kathmandu Valley ◽

So2 Emissions ◽

Emissions Inventory ◽

Brick Kilns ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations ◽

Local Emissions

Abstract. Air pollution is one of the most pressing environmental issues in the Kathmandu Valley, where the capital city of Nepal is located. We estimated emissions from two of the major source types in the valley (vehicles and brick kilns) and analyzed the corresponding impacts on regional air quality. First, we estimated the on-road vehicle emissions in the valley using the International Vehicle Emissions (IVE) model with local emissions factors and the latest available data for vehicle registration. We also identified the locations of the brick kilns in the Kathmandu Valley and developed an emissions inventory for these kilns using emissions factors measured during the Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE) field campaign in April 2015. Our results indicate that the commonly used global emissions inventory, the Hemispheric Transport of Air Pollution (HTAP_v2.2), underestimates particulate matter emissions from vehicles in the Kathmandu Valley by a factor greater than 100. HTAP_v2.2 does not include the brick sector and we found that our sulfur dioxide (SO2) emissions estimates from brick kilns are comparable to 70 % of the total SO2 emissions considered in HTAP_v2.2. Next, we simulated air quality using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) for April 2015 based on three different emissions scenarios: HTAP only, HTAP with updated vehicle emissions, and HTAP with both updated vehicle and brick kilns emissions. Comparisons between simulated results and observations indicate that the model underestimates observed surface elemental carbon (EC) and SO2 concentrations under all emissions scenarios. However, our updated estimates of vehicle emissions significantly reduced model bias for EC, while updated emissions from brick kilns improved model performance in simulating SO2. These results highlight the importance of improving local emissions estimates for air quality modeling. We further find that model overestimation of surface wind leads to underestimated air pollutant concentrations in the Kathmandu Valley. Future work should focus on improving local emissions estimates for other major and underrepresented sources (e.g., crop residue burning and garbage burning) with a high spatial resolution, as well as the model's boundary-layer representation, to capture strong spatial gradients of air pollutant concentrations.

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Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

Applied Sciences ◽

10.3390/app9194069 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4069 ◽

Cited By ~ 2

Author(s):

Huixiang Liu ◽

Qing Li ◽

Dongbing Yu ◽

Yu Gu

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Air Quality ◽

Regression Models ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

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Land use regression modelling of ambient air pollutants in Hong Kong

10.5194/egusphere-egu2020-12648 ◽

2020 ◽

Author(s):

Zhiyuan Li ◽

Steve Hung Lam Yim ◽

Kin-Fai Ho

Keyword(s):

Land Use ◽

Hong Kong ◽

Air Quality ◽

Air Pollutants ◽

Ambient Air ◽

Air Pollutant ◽

High Rise ◽

Pollutant Concentrations ◽

Air Quality Measurements ◽

Air Pollutant Concentrations

<p>Land use regression (LUR) models estimate air pollutant concentrations for areas without air quality measurements, which provides valuable information for exposure assessment and epidemiological studies. In the present study, we developed LUR models for ambient air pollutants in Hong Kong, China, a typical high-density and high-rise city. Air quality measurements at sixteen air quality monitoring stations, operated by the Hong Kong Environmental Protection Department, were collected. Moreover, five categories of predictor variables, including population distribution, traffic emissions, land use variables, urban/building morphology, and meteorological parameters, were employed to establish the LUR models of various air pollutants. Then the spatial distribution of air pollutant concentrations at 1 km &#215; 1 km grid cells were plotted. Taking fine particle (PM2.5) as an example, the developed LUR model explained 89% of variability of PM2.5 concentrations, with a leave-one-out-cross-validation R2 of 0.64. LUR modelling results for other air pollutants will be presented. In addition, further improvements on the development of LUR models will be discussed. This study can help to assess long-term exposures to air pollutants for high-density and high-rise urban areas like Hong Kong.</p>

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Results of the California Healthy Homes Indoor Air Quality Study of 2011-2013: impact of natural gas appliances on air pollutant concentrations

Indoor Air ◽

10.1111/ina.12190 ◽

2015 ◽

Vol 26 (2) ◽

pp. 231-245 ◽

Cited By ~ 22

Author(s):

N. A. Mullen ◽

J. Li ◽

M. L. Russell ◽

M. Spears ◽

B. D. Less ◽

...

Keyword(s):

Air Quality ◽

Natural Gas ◽

Indoor Air Quality ◽

Indoor Air ◽

Air Pollutant ◽

Healthy Homes ◽

Pollutant Concentrations ◽

Quality Study ◽

Air Pollutant Concentrations

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Mobile-platform measurement of air pollutant concentrations in California: performance assessment, statistical methods for evaluating spatial variations, and spatial representativeness

Atmospheric Measurement Techniques ◽

10.5194/amt-13-3277-2020 ◽

2020 ◽

Vol 13 (6) ◽

pp. 3277-3301

Author(s):

Paul A. Solomon ◽

Dena Vallano ◽

Melissa Lunden ◽

Brian LaFranchi ◽

Charles L. Blanchard ◽

...

Keyword(s):

Air Quality ◽

Los Angeles ◽

San Francisco ◽

Spatial Variations ◽

Air Pollutant ◽

Mobile Platform ◽

Diurnal Variability ◽

Pollutant Concentrations ◽

Air Pollutant Concentrations ◽

Mobile Measurements

Abstract. Mobile-platform measurements provide new opportunities for characterizing spatial variations in air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc., mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study. On-road measurements of air quality were made during a series of sampling campaigns between May 2016 and September 2017 at high (i.e., 1 s) temporal and spatial resolution at several California locations: Los Angeles, San Francisco, and the northern San Joaquin Valley (including nonurban roads and the cities of Tracy, Stockton, Manteca, Merced, Modesto, and Turlock). The results demonstrate that the approach is effective for quantifying spatial variations in air pollutant concentrations over measurement periods as short as 2 weeks. Measurement accuracy and precision are evaluated using results of weekly performance checks and periodic audits conducted through the sampler inlets, which show that research instruments located within stationary vehicles are capable of reliably measuring nitric oxide (NO), nitrogen dioxide (NO2), ozone (O3), methane (CH4), black carbon (BC), and particle number (PN) concentration, with bias and precision ranging from < 10 % for gases to < 25 % for BC and PN at 1 s time resolution. The quality of the mobile measurements in the ambient environment is examined by comparisons with data from an adjacent (< 9 m) stationary regulatory air quality monitoring site and by paired collocated vehicle comparisons, both stationary and driving. The mobile measurements indicate that United States Environmental Protection Agency (US EPA) classifications of two Los Angeles stationary regulatory monitors' scales of representation are appropriate. Paired time-synchronous mobile measurements are used to characterize the spatial scales of concentration variations when vehicles were separated by < 1 to 10 km. A data analysis approach is developed to characterize spatial variations while limiting the confounding influence of diurnal variability. The approach is illustrated using data from San Francisco, revealing 1 km scale differences in mean NO2 and O3 concentrations up to 117 % and 46 %, respectively, of mean values during a 2-week sampling period. In San Francisco and Los Angeles, spatial variations up to factors of 6 to 8 occur at sampling scales of 100–300 m, corresponding to 1 min averages.

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Impact of the COVID-19 Outbreak on Air Quality in Korea

Atmosphere ◽

10.3390/atmos11101137 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1137 ◽

Cited By ~ 3

Author(s):

Ji Hoon Seo ◽

Hyun Woo Jeon ◽

Ui Jae Sung ◽

Jong-Ryeul Sohn

Keyword(s):

Air Quality ◽

Human Activity ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Control Action ◽

Quality Improvements ◽

Social Distancing ◽

Two Cities ◽

Activity Restrictions

The COVID-19 pandemic has led countries to take action, which has included practicing social distancing or lockdown. Many cities are experiencing air quality improvements due to human activity restrictions. The purpose of this study was to compare the air quality between 2020 and the previous three years, focusing on the two cities (Seoul and Daegu) where coronavirus is spreading the fastest in Korea. Significant decreases in PM2.5, PM10, CO, and NO2 were observed in both cities. In particular, compared to the same period of 2017-2019, in March 2020, PM2.5 showed remarkable reductions of 36% and 30% in Seoul and Daegu, respectively. The effects of social distancing have maximized improvements in air quality due to reduced transboundary pollutants. The PM2.5/PM10 ratio was significantly reduced after social distancing, indicating that the contribution of traffic-related PM2.5 declined. Air quality improved overall from January to July, and the most noticeable drop in the air quality index (AQI) was observed in April. These findings indicate that relatively weak social distancing measures compared to a COVID-19 lockdown can help reduce air pollutant levels. At the same time, however, changes in air quality in the neighboring countries caused by COVID-19 control action are affecting Korea.

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30 Years of Air Quality Trends in Japan

Atmosphere ◽

10.3390/atmos12081072 ◽

2021 ◽

Vol 12 (8) ◽

pp. 1072

Author(s):

Akiyoshi Ito ◽

Shinji Wakamatsu ◽

Tazuko Morikawa ◽

Shinji Kobayashi

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutant ◽

Control Measures ◽

Related Substances ◽

Pollutant Concentrations ◽

Exhaust Particles ◽

Air Pollutant Concentrations ◽

Atmospheric Concentrations ◽

Pollution Concentrations

The aim of this paper is to obtain information that will contribute to measures and research needed to further improve the air quality in Japan. The trends and characteristics of air pollutant concentrations, especially PM2.5, ozone, and related substances, over the past 30 years, are analyzed, and the relationships between concentrations and emissions are discussed quantitatively. We found that PM2.5 mass concentrations have decreased, with the largest reduction in elemental carbon (EC) as the PM2.5 component. The concentrations of organic carbon (OC) have not changed significantly compared to other components, suggesting that especially VOC emissions as precursors need to be reduced. In addition, the analysis of the differences in PM2.5 concentrations between the ambient and the roadside showed that further research on non-exhaust particles is needed. For NOx and SO2, there is a linear relationship between domestic anthropogenic emissions and atmospheric concentrations, indicating that emission control measures are directly effective in the reduction in concentrations. Also, recent air pollution episodes and the effect of reduced economic activity, as a consequence of COVID-19, on air pollution concentrations are summarized.

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