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.

scholarly journals Associations of Ambient Air Pollutants and Meteorological Factors With COVID-19 Transmission in 31 Chinese Provinces: A Time Series Study

2021 ◽  
Vol 58 ◽  
pp. 004695802110602
Author(s):  
Han Cao ◽  
Bingxiao Li ◽  
Tianlun Gu ◽  
Xiaohui Liu ◽  
Kai Meng ◽  
...  
Keyword(s):  
Air Pollution ◽  
Relative Humidity ◽  
Wind Velocity ◽  
Air Pollutants ◽  
Meteorological Factors ◽  
Meta Analysis ◽  
Ambient Air ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Evidence regarding the effects of environmental factors on COVID-19 transmission is mixed. We aimed to explore the associations of air pollutants and meteorological factors with COVID-19 confirmed cases during the outbreak period throughout China. The number of COVID-19 confirmed cases, air pollutant concentrations, and meteorological factors in China from January 25 to February 29, 2020, (36 days) were extracted from authoritative electronic databases. The associations were estimated for a single-day lag as well as moving averages lag using generalized additive mixed models. Region-specific analyses and meta-analysis were conducted in 5 selected regions from the north to south of China with diverse air pollution levels and weather conditions and sufficient sample size. Nonlinear concentration–response analyses were performed. An increase of each interquartile range in PM2.5, PM10, SO2, NO2, O3, and CO at lag4 corresponded to 1.40 (1.37–1.43), 1.35 (1.32–1.37), 1.01 (1.00–1.02), 1.08 (1.07–1.10), 1.28 (1.27–1.29), and 1.26 (1.24–1.28) ORs of daily new cases, respectively. For 1°C, 1%, and 1 m/s increase in temperature, relative humidity, and wind velocity, the ORs were 0.97 (0.97–0.98), 0.96 (0.96–0.97), and 0.94 (0.92–0.95), respectively. The estimates of PM2.5, PM10, NO2, and all meteorological factors remained significantly after meta-analysis for the five selected regions. The concentration–response relationships showed that higher concentrations of air pollutants and lower meteorological factors were associated with daily new cases increasing. Higher air pollutant concentrations and lower temperature, relative humidity and wind velocity may favor COVID-19 transmission. Controlling ambient air pollution, especially for PM2.5, PM10, NO2, may be an important component of reducing risk of COVID-19 infection. In addition, as winter months are arriving in China, the meteorological factors may play a negative role in prevention. Therefore, it is significant to implement the public health control measures persistently in case another possible pandemic.

scholarly journals The role of meteorological conditions and pollution control strategies in reducing air pollution in Beijing during APEC 2014 and Victory Parade 2015

2017 ◽  
Vol 17 (22) ◽  
pp. 13921-13940 ◽  
Author(s):  
Pengfei Liang ◽  
Tong Zhu ◽  
Yanhua Fang ◽  
Yingruo Li ◽  
Yiqun Han ◽  
...  
Keyword(s):  
Air Pollution ◽  
Pollution Control ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Control Strategies ◽  
Meteorological Conditions ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations

Abstract. To control severe air pollution in China, comprehensive pollution control strategies have been implemented throughout the country in recent years. To evaluate the effectiveness of these strategies, the influence of meteorological conditions on levels of air pollution needs to be determined. Using the intensive air pollution control strategies implemented during the Asia-Pacific Economic Cooperation Forum in 2014 (APEC 2014) and the 2015 China Victory Day Parade (Victory Parade 2015) as examples, we estimated the role of meteorological conditions and pollution control strategies in reducing air pollution levels in Beijing. Atmospheric particulate matter of aerodynamic diameter  ≤ 2.5 µm (PM2.5) samples were collected and gaseous pollutants (SO2, NO, NOx, and O3) were measured online at a site in Peking University (PKU). To determine the influence of meteorological conditions on the levels of air pollution, we first compared the air pollutant concentrations during days with stable meteorological conditions. However, there were few days with stable meteorological conditions during the Victory Parade. As such, we were unable to estimate the level of emission reduction efforts during this period. Finally, a generalized linear regression model (GLM) based only on meteorological parameters was built to predict air pollutant concentrations, which could explain more than 70 % of the variation in air pollutant concentration levels, after incorporating the nonlinear relationships between certain meteorological parameters and the concentrations of air pollutants. Evaluation of the GLM performance revealed that the GLM, even based only on meteorological parameters, could be satisfactory to estimate the contribution of meteorological conditions in reducing air pollution and, hence, the contribution of control strategies in reducing air pollution. Using the GLM, we found that the meteorological conditions and pollution control strategies contributed 30 and 28 % to the reduction of the PM2.5 concentration during APEC and 38 and 25 % during the Victory Parade, respectively, based on the assumption that the concentrations of air pollutants are only determined by meteorological conditions and emission intensities. We also estimated the contribution of meteorological conditions and control strategies in reducing the concentrations of gaseous pollutants and PM2.5 components with the GLMs, revealing the effective control of anthropogenic emissions.

scholarly journals The Multi-Time Scale Changes in Air Pollutant Concentrations and Its Mechanism before and during the COVID-19 Periods: A Case Study from Guiyang, Guizhou Province

Atmosphere ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 1490
Author(s):  
Zhihua Su ◽  
Xin Li ◽  
Yunlong Liu ◽  
Bing Deng
Keyword(s):  
Air Pollution ◽  
Time Scale ◽  
Air Pollutants ◽  
Photochemical Reactions ◽  
Air Pollutant ◽  
Guizhou Province ◽  
Air Pollution Control ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
The Impact

The lockdown during the coronavirus disease 2019 (COVID-19) pandemic provides a scarce opportunity to assess the efficiency of air pollution mitigation. Herein, the monitoring data of air pollutants were thoroughly analyzed together with meteorological parameters to explore the impact of human activity on the multi-time scale changes of air pollutant concentrations in Guiyang city, located in Southwest China. The results show that the COVID-19 lockdown had different effects on the criteria air pollutants, i.e., PM2.5 (diameter ≤ 2.5 μm), PM10 (diameter ≤ 10 μm), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) concentrations. The lockdown caused a significant drop in NO2 concentration. During the first-level lockdown period, the NO2 concentration declined sharply by 8.41 μg·m−3 (45.68%). The decrease in NO concentration caused the “titration effect” to weaken, leading to a sharp increase in O3 concentration. Although human activities resumed partially and the “titration effect” enhanced certainly during the second-level lockdown period, the meteorological conditions became more conducive to the formation of O3 by photochemical reactions. Atmosphere oxidation was enhanced to promote the generation of secondary aerosols through gas–particle transitions, thus compensating for the reduced primary emission of PM2.5. The implication of this study is that the appropriate air pollution control policies must be initiated to suppress the secondary generation of both PM2.5 and O3.

scholarly journals Correlations between air pollutant concentrations in selected urban and rural areas in Poland

2019 ◽  
pp. 14-22
Author(s):  
Artur Stelęgowski
Keyword(s):  
Air Pollution ◽  
Population Density ◽  
Linear Correlation ◽  
Rural Areas ◽  
Air Pollutants ◽  
Air Pollutant ◽  
Urban Background ◽  
Pollutant Concentrations ◽  
Air Pollutant Concentrations ◽  
Urban And Rural Areas

Correlations between concentrations of selected air pollutants were analyzed in different areas in central Poland from 2012-2016. Three neighboring voivodeships (Lower Silesian, Lodz, and Masovian), were selected for which specific measurement locations were designated in urban and rural areas. The characteristics of the location of monitoring stations allowed to distinguish the following types of measurement stations: “urbantransport”, “urban-background", "suburban-background", "town-background", and "rural-background". Therefore, using the Pearson's linear correlation coefficient, it was possible to analyze the interrelations between the occurrence of air pollution in various types of areas. It was found that the coefficient changed along with the type of area. Moreover, it turned out that the coefficient decreased in each voivodeship along with a decrease in the population density of the analyzed areas. In addition, concentrations of various air pollutants in given areas were compared. Also, it was observed that the strongest correlations occur between the results of calculations from measurement stations located in the same province.

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 Intervention analysis with autoregressive integrated moving average models for time series of urban air pollutants

2021 ◽  
Vol 2139 (1) ◽  
pp. 012002
Author(s):  
L A Manco-Perdomo ◽  
L A Pérez-Padilla ◽  
C A Zafra-Mejía
Keyword(s):  
Air Pollutants ◽  
Moving Average ◽  
Air Pollutant ◽  
Urban Air ◽  
Intervention Analysis ◽  
Autoregressive Integrated Moving Average ◽  
Pollutant Concentrations ◽  
Moving Average Models ◽  
Regulatory Interventions ◽  
Air Pollutant Concentrations

Abstract The objective of this paper is to show an intervention analysis with autoregressive integrated moving average models for time series of air pollutants in a Latin American megacity. The interventions considered in this study correspond to public regulations for the control of urban air quality. The study period comprised 10 years. Information from 10 monitoring stations distributed throughout the megacity was used. Modelling showed that setting maximum emission limits for different pollution sources and improving fuel were the most appropriate regulatory interventions to reduce air pollutant concentrations. Modelling results also suggested that these interventions began to be effective between the first 4 days-15 days after their publication. The models developed on a monthly timescale had a short autoregressive memory. The air pollutant concentrations at a given time were influenced by the concentrations of up to three months immediately preceding. Moving average term of the models showed fluctuations in time of the air pollutant concentrations (3 months - 14 months). Within the framework of the applications of physics for the air pollution control, this study is relevant for the following findings: the usefulness of autoregressive integrated moving average models to temporal simulate air pollutants, and for its suitable performance to detect and quantify regulatory interventions.

scholarly journals Long-term trends of air pollutant concentrations in Poland

2019 ◽  
Vol 116 ◽  
pp. 00027
Author(s):  
Szymon Hoffman
Keyword(s):  
Least Squares Method ◽  
Monitoring Network ◽  
Air Pollutant ◽  
Pollutant Concentrations ◽  
Hourly Data ◽  
Long Time ◽  
Long Term Trends ◽  
Air Pollutant Concentrations ◽  
Term Data

The assessment of changes in air pollution quality for 4 selected sites in Southern and Central Poland was presented in this paper. The evaluation was based on the sets of long-term data, recorded by the state air monitoring network. Concentrations of O3, PM10, SO2, NOx, and CO, were considered. The basis for the calculations were 12-year time series of hourly concentrations. Using the hourly data, the monthly averages were calculated to illustrate seasonal changes of pollutant concentrations. Linear trends were adjusted to the concentration courses with the least squares method. Long-time trends were calculated for each pollutant separately. Based on the analysis of the trend lines slopes, risks those may arise in the future were identified.

Land use regression modelling of ambient air pollutants in Hong Kong

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 × 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>

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.

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