scholarly journals Spatial Regression Modeling Approach for Assessing the Spatial Variation of Air Pollutants

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 785
Author(s):  
Seunghoon Park ◽  
Dongwon Ko
Keyword(s):  
Built Environment ◽  
Residential Buildings ◽  
Spatial Regression ◽  
Air Pollutant ◽  
Single Family ◽  
Socioeconomic Characteristics ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Industrial Zones

Over the past decades, industrialization has resulted in radical economic development in Korea. The resulting urban sprawl and unsustainable development have led to considerable air pollution. In this study, using spatial regression models, we examine the effects of the physical and socioeconomic characteristics of neighborhoods on particulate matter (PM10, PM2.5), NO2, CO, and SO2 concentrations in the Daegu Metropolitan area. Results reveal the following: (i) the socioeconomic characteristics were not statistically significant regardless of the air pollutant type; (ii) the effects of the built environment characteristics of the neighborhoods were different for each air pollutant. Compared with other pollutants, PM2.5 was affected more by the built environment. Concerning the neighborhoods’ main roads, the SO2 concentration was higher, that of PM2.5 was higher in neighborhoods with more bus stops, and those of CO and PM2.5 were possibly higher in the neighborhood of industrial zones. In neighborhoods with parks and green areas, air pollutant concentrations are likely to be lower. When the total used surface of residential buildings was higher, the air pollutant concentrations were lower. Contextually, similar neighborhoods with more single-family houses seemed to have high pollution levels. Overall, this study is expected to guide policymakers and planners in making smart decisions for eco-friendly and healthy cities.

scholarly journals Aggravation of CoVID-19 infections due to air pollutant concentrations in Indian cities

2021 ◽  
Author(s):  
Arnab Mondal ◽  
Asha Sunilkumar ◽  
Shishir Kumar Singh ◽  
Surajit Mondal ◽  
Amit Kumar Mondal
Keyword(s):  
Air Pollutants ◽  
Time Frame ◽  
Air Pollutant ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
The Government ◽  
Air Pollutant Concentrations ◽  
High Level ◽  
In The Beginning ◽  
Indian Cities

Abstract In the beginning of March 2020, cases of CoVID-19 infections began rising worldwide, reacting to which the Government of India called for nationwide lockdown initially for March 25th to April 14th, 2020 and later extended it in phases till May 31st, 2020. Due to the forced restrictions pan-India on every level, the move led to drastic drop in pollution levels. The results demonstrated that during lockdown air quality is significantly improved and the pollution levels of PM2.5, PM10, SO2 and NO2 reduced drastically during the lockdown period than the preceding year for the same time frame. A direct relationship has been established between the high level of air pollutants (PM2.5, PM10, NO2 and SO2) and CoVID-19 infections being reported in these Indian cities. The correlation indicates that the air pollutants like PM2.5, PM10, NO2 and SO2 are aggravating the number of casualties due to the CoVID-19 infections. The high-level exposure of PM2.5 over a long period is found to be significantly correlated with the mortality per unit confirmed CoVID-19 cases as compared to other air pollutant parameters like PM10, NO2 and SO2.

scholarly journals Measurement Report: A Multi-Year Study on the Impacts of Chinese New Year Celebrations on Air Quality in Beijing, China

2021 ◽  
Author(s):  
Benjamin Foreback ◽  
Lubna Dada ◽  
Kaspar Dällenbach ◽  
Chao Yan ◽  
Lili Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Sulfur Dioxide ◽  
Sulfuric Acid Concentration ◽  
Quality Measurement ◽  
Air Pollutant ◽  
Chinese Government ◽  
Pollution Levels ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Spectrometer Data

Abstract. We investigated the influence of the Chinese New Year (CNY) celebrations on local air quality in Beijing from 2013 through 2019, bringing together comprehensive observations at the newly-constructed Aerosol and Haze Laboratory at Beijing University of Chemical Technology – West Campus (BUCT-AHL) and data from Chinese government air quality measurement stations. In this study, these datasets are used together to provide a detailed analysis of air quality during the CNY over multiple years. Before CNY in 2018, the city of Beijing prohibited the use of fireworks and firecrackers in an effort to reduce air pollution. In 2018 air pollutant concentrations still showed a significant peak during the CNY night, even though not as strong as in previous years, but in 2019, the pollution levels were notably lower. During the studied 7-year study period, it appears that there has been a long-term decrease in CNY related emissions since 2016. Based on our analysis, the pollutants with the most notable spike during CNY were sulfur dioxide and particulate matter, including black carbon. Sulfuric acid concentration followed the sulfur dioxide concentration and showed elevated overnight concentration in CNY 2018, but not notably in 2019. Additionally, spectrometer data and analysis of aerosol particle number size distribution shows direct emissions of particles with diameters around 20 nm during CNY in 2018 and 2019. Meteorological conditions were comparable between the latest two years, indicating that air quality associated with the CNY may be improving, perhaps a positive effect of the restrictions. The longer observations in the future will provide confirmation for these trends.

scholarly journals Modelling of Urban Air Pollutant Concentrations with Artificial Neural Networks Using Novel Input Variables

2020 ◽  
Vol 17 (6) ◽  
pp. 2025
Author(s):  
Laura Goulier ◽  
Bastian Paas ◽  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Air ◽  
Pollutant Concentrations ◽  
Day Of The Week ◽  
Artificial Neural ◽  
Input Variables ◽  
Air Pollutant Concentrations ◽  
Artificial Neural Network Ann ◽  
Performance Results

Since operating urban air quality stations is not only time consuming but also costly, and because air pollutants can cause serious health problems, this paper presents the hourly prediction of ten air pollutant concentrations (CO2, NH3, NO, NO2, NOx, O3, PM1, PM2.5, PM10 and PN10) in a street canyon in Münster using an artificial neural network (ANN) approach. Special attention was paid to comparing three predictor options representing the traffic volume: we included acoustic sound measurements (sound), the total number of vehicles (traffic), and the hour of the day and the day of the week (time) as input variables and then compared their prediction powers. The models were trained, validated and tested to evaluate their performance. Results showed that the predictions of the gaseous air pollutants NO, NO2, NOx, and O3 reveal very good agreement with observations, whereas predictions for particle concentrations and NH3 were less successful, indicating that these models can be improved. All three input variable options (sound, traffic and time) proved to be suitable and showed distinct strengths for modelling various air pollutant concentrations.

scholarly journals Analysis of Temporal Variations of Air Pollutant Concentrations in Ogoni Area, Niger Delta Region, Nigeria

2021 ◽  
pp. 63-73
Author(s):  
B. Yorkor ◽  
T. G. Leton ◽  
J. N. Ugbebor
Keyword(s):  
Air Pollution ◽  
Temporal Variability ◽  
Niger Delta ◽  
Air Pollutants ◽  
Temporal Variations ◽  
Time Of Day ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Air Pollutant Concentrations

This study investigated the temporal variations of air pollutant concentrations in Ogoni area, Niger Delta, Nigeria. The study used hourly data measured over 8 hours for 12 months at selected locations within the area. The analyses were based on time series and time variations techniques in Openair packages of R programming software. The variations of air pollutant concentrations by time of day and days of week were simulated. Hours of the day, days of the week and monthly variations were graphically simulated. Variations in the mean concentrations of air pollutants by time were determined at 95 % confidence intervals. Sulphur dioxide (SO2), Nitrogen dioxide (NO2), ground level Ozone (O3) and fine particulate matter (PM2.5) concentrations exceeded permissible standards. Air pollutant concentrations showed increase in January, February, November and December compared to other months. Simulation showed that air pollutants varied significantly by hours-of-the-day and days-of-the-week and months-of-the-year. Analysis of temporal variability revealed that air pollutant concentrations increased during weekdays and decreased during weekends. The temporal variability of air pollutants in Ogoni area showed that anthropogenic activities were the main sources of air pollution in the area, therefore further studies are required to determine air pollutant dispersion pattern and evaluation the potential sources of air pollution in the area.

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