scholarly journals An analysis of air pollution at some industrial areas of Kano using the AERMOD Model

2020 ◽  
Vol 12 (1) ◽  
pp. 117-127
Author(s):  
Maryam Idris ◽  
T.H. Darma ◽  
F.S. Koki ◽  
A. Suleiman ◽  
M.H. Ali ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Model Evaluation ◽  
Air Pollutant ◽  
Percentage Error ◽  
Poor Correlation ◽  
Dynamical Equations ◽  
Model Skill ◽  
Aermod Model

The effect of pollution on air quality has been a concern for mankind for a long time. In some cases the problem is essentially one of local emissions in a given urban area leading to an adverse effect on air quality in that same area. However, in the general case, the problem is more diverse in that the problem of air pollution has multiplicity effects beyond the point source and these effects are dynamic in nature. Such effects are usually evaluated using dynamical equations. In this study, a comprehensive review on effect of air polluting variables was described on the basis of evaluation of formulation equations of the American Meteorological Society and U.S. Environmental protection Agency Regulatory Model (AERMOD view 9.6.5). The AERMOD model was also used to simulate the dispersion and deposition of the hourly and daily H2S and NO2 concentrations from two domains: Challawa and Sharada industrial estates /areas respectively. The AERMOD model evaluation showed that there was good correlation between the modelled and observed H2S concentration for the daily and hourly comparison at Challawa  (0.53 and 0.91 respectively) but the daily and hourly comparison of H2S at Sharada (0.13 and 0.46 respectively) was seen to drop indicating poor correlation and model skill. However, model evaluation of NO2 shows poor agreements and model skill at Challawa as well as daily comparison at Sharada. However, the modelling shows good agreement (R2= 0.64) in the trend for the hourly value modelled versus observed concentrations at Sharada. Moreover, the mean absolute percentage error (MAPE) for the two pollutants (H2S and NO2) at all the two domains indicates highly accurate result for both daily and hourly concentrations. AERMOD software can therefore be used to estimate the dispersion and deposition of the pollutants at some domains considered in this study. Key Words: AERMOD model, Air pollutant, Industrial sources, Dispersion and Deposition

Federal Air-Quality Standards and Their Implementation

1976 ◽  
Vol 1 (2) ◽  
pp. 365-409 ◽  
Author(s):  
David P. Currie
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Motor Vehicle ◽  
Quality Standards ◽  
Air Pollutant ◽  
Federal State ◽  
Air Pollution Control ◽  
Air Quality Standards ◽  
Stationary Sources

With the enactment of the Clean Air Act amendments in 1970, the federal government, essentially an interested bystander as recently as 1960, became the dominant presence in air pollution control. The current statute provides for federal research, financial support of state control programs, and interstate compacts (secs. 101-106). It retains, in vestigial form, a cumbersome conference procedure (sec. 115) copied from the earlier water-pollution statute and never much used in air pollution. irect federal regulatory authority was substantially increased by the 1970 amendments: the federal Environmental Protection Agency (EPA) may now adopt emission standards not only for new vehicles (sec. 202) but also for all aircraft (sec. 231), for new stationary sources of any type that “may contribute significantly to air pollution” (sec. 111), and for any source of a “hazardous” air pollutant, that is, one that “may cause, or contribute to, an increase in mortality or … in serious irreversible, or incapacitating reversible, illness” (sec. 1 12); it may also regulate the contents of motor-vehicle fuels (sec. 211). An emergency provision authorizes the federal agency, absent adequate state or local action, to sue to enjoin any emissions contributing to “an imminent and substantial endangerment to the health of persons” (sec. 303). Yet everyday control of most existing stationary sources remains subject to an awkward joint federal-state system of air-quality standards. That system is the subject of the present article.

scholarly journals Quantifying Air Pollutant Variations during COVID-19 Lockdown in a Capital City in Northwest China

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 788
Author(s):  
Rong Feng ◽  
Hongmei Xu ◽  
Zexuan Wang ◽  
Yunxuan Gu ◽  
Zhe Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Rural Areas ◽  
Vehicle Emissions ◽  
Motor Vehicle ◽  
Northwest China ◽  
Air Pollutant ◽  
Capital City ◽  
Air Pollution Control ◽  
Motor Vehicle Emissions

In the context of the outbreak of coronavirus disease 2019 (COVID-19), strict lockdown policies were implemented to control nonessential human activities in Xi’an, northwest China, which greatly limited the spread of the pandemic and affected air quality. Compared with pre-lockdown, the air quality index and concentrations of PM2.5, PM10, SO2, and CO during the lockdown reduced, but the reductions were not very significant. NO2 levels exhibited the largest decrease (52%) during lockdown, owing to the remarkable decreased motor vehicle emissions. The highest K+ and lowest Ca2+ concentrations in PM2.5 samples could be attributed to the increase in household biomass fuel consumption in suburbs and rural areas around Xi’an and the decrease in human physical activities in Xi’an (e.g., human travel, vehicle emissions, construction activities), respectively, during the lockdown period. Secondary chemical reactions in the atmosphere increased in the lockdown period, as evidenced by the increased O3 level (increased by 160%) and OC/EC ratios in PM2.5 (increased by 26%), compared with pre-lockdown levels. The results, based on a natural experiment in this study, can be used as a reference for studying the formation and source of air pollution in Xi’an and provide evidence for establishing future long-term air pollution control policies.

scholarly journals Gender Differences in Association between Air Pollution and Daily Mortality in the Capital of the Green Lungs of Poland–Population-Based Study with 2,953,000 Person-Years of Follow-Up

2020 ◽  
Vol 9 (8) ◽  
pp. 2351
Author(s):  
Łukasz Kuźma ◽  
Krzysztof Struniawski ◽  
Szymon Pogorzelski ◽  
Hanna Bachórzewska-Gajewska ◽  
Sławomir Dobrzycki
Keyword(s):  
Public Health ◽  
Gender Differences ◽  
Air Pollution ◽  
Air Quality ◽  
Air Pollutant ◽  
World Health ◽  
The European Union ◽  
Daily Mortality ◽  
Male Population ◽  
The Impact

(1) Introduction: air pollution is considered to be one of the main risk factors for public health. According to the European Environment Agency (EEA), air pollution contributes to the premature deaths of approximately 500,000 citizens of the European Union (EU), including almost 5000 inhabitants of Poland every year. (2) Purpose: to assess the gender differences in the impact of air pollution on the mortality in the population of the city of Bialystok—the capital of the Green Lungs of Poland. (3) Materials and Methods: based on the data from the Central Statistical Office, the number—and causes of death—of Białystok residents in the period 2008–2017 were analyzed. The study utilized the data recorded by the Provincial Inspectorate for Environmental Protection station and the Institute of Meteorology and Water Management during the analysis period. Time series regression with Poisson distribution was used in statistical analysis. (4) Results: A total of 34,005 deaths had been recorded, in which women accounted for 47.5%. The proportion of cardiovascular-related deaths was 48% (n = 16,370). An increase of SO2 concentration by 1-µg/m3 (relative risk (RR) 1.07, 95% confidence interval (CI) 1.02–1.12; p = 0.005) and a 10 °C decrease of temperature (RR 1.03, 95% CI 1.01–1.05; p = 0.005) were related to an increase in the number of daily deaths. No gender differences in the impact of air pollution on mortality were observed. In the analysis of the subgroup of cardiovascular deaths, the main pollutant that was found to have an effect on daily mortality was particulate matter with a diameter of 2.5 μm or less (PM2.5); the RR for 10-µg/m3 increase of PM2.5 was 1.07 (95% CI 1.02–1.12; p = 0.01), and this effect was noted only in the male population. (5) Conclusions: air quality and atmospheric conditions had an impact on the mortality of Bialystok residents. The main air pollutant that influenced the mortality rate was SO2, and there were no gender differences in the impact of this pollutant. In the male population, an increased exposure to PM2.5 concentration was associated with significantly higher cardiovascular mortality. These findings suggest that improving air quality, in particular, even with lower SO2 levels than currently allowed by the World Health Organization (WHO) guidelines, may benefit public health. Further studies on this topic are needed, but our results bring questions whether the recommendations concerning acceptable concentrations of air pollutants should be stricter, or is there a safe concentration of SO2 in the air at all.

scholarly journals Use of electrochemical sensors for measurement of air pollution: correcting interference response and validating measurements

2017 ◽  
Vol 10 (9) ◽  
pp. 3575-3588 ◽  
Author(s):  
Eben S. Cross ◽  
Leah R. Williams ◽  
David K. Lewis ◽  
Gregory R. Magoon ◽  
Timothy B. Onasch ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Lower Cost ◽  
Electrochemical Sensors ◽  
Air Pollutant ◽  
Sensor System ◽  
Time Interval ◽  
High Dimensional Model Representation ◽  
Integrated Sensor ◽  
Mean Square Errors

Abstract. The environments in which we live, work, and play are subject to enormous variability in air pollutant concentrations. To adequately characterize air quality (AQ), measurements must be fast (real time), scalable, and reliable (with known accuracy, precision, and stability over time). Lower-cost air-quality-sensor technologies offer new opportunities for fast and distributed measurements, but a persistent characterization gap remains when it comes to evaluating sensor performance under realistic environmental sampling conditions. This limits our ability to inform the public about pollution sources and inspire policy makers to address environmental justice issues related to air quality. In this paper, initial results obtained with a recently developed lower-cost air-quality-sensor system are reported. In this project, data were acquired with the ARISense integrated sensor package over a 4.5-month time interval during which the sensor system was co-located with a state-operated (Massachusetts, USA) air quality monitoring station equipped with reference instrumentation measuring the same pollutant species. This paper focuses on validating electrochemical (EC) sensor measurements of CO, NO, NO2, and O3 at an urban neighborhood site with pollutant concentration ranges (parts per billion by volume, ppb; 5 min averages, ±1σ): [CO]  =  231 ± 116 ppb (spanning 84–1706 ppb), [NO]  =  6.1 ± 11.5 ppb (spanning 0–209 ppb), [NO2]  =  11.7 ± 8.3 ppb (spanning 0–71 ppb), and [O3]  =  23.2 ± 12.5 ppb (spanning 0–99 ppb). Through the use of high-dimensional model representation (HDMR), we show that interference effects derived from the variable ambient gas concentration mix and changing environmental conditions over three seasons (sensor flow-cell temperature  =  23.4 ± 8.5 °C, spanning 4.1 to 45.2 °C; and relative humidity  =  50.1 ± 15.3 %, spanning 9.8–79.9 %) can be effectively modeled for the Alphasense CO-B4, NO-B4, NO2-B43F, and Ox-B421 sensors, yielding (5 min average) root mean square errors (RMSE) of 39.2, 4.52, 4.56, and 9.71 ppb, respectively. Our results substantiate the potential for distributed air pollution measurements that could be enabled with these sensors.

scholarly journals Case Study on Anthropogenic Air Pollution in Nagpur City

2021 ◽  
Vol 9 (VI) ◽  
pp. 2279-2282
Author(s):  
Mukul Dayaramani
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Harmful Effect ◽  
Central India ◽  
Well Being ◽  
Good Health ◽  
Air Pollutant ◽  
Corporate Community ◽  
Health And Well Being

Air pollution is a very serious problem worldwide. Anthropogenic air pollution is mostly related to the combustion of various types of fuels. Air pollutant levels remain too high and air quality problems are still not solved. The presence of pollutants in the air has a harmful effect on the human health and the environment. Good air quality is a prerequisite for our good health and well-being. Nagpur city is located in Maharashtra state of central India. Business hub and increased industrialization in study area is affecting the environment adversely. n. Changing life style of corporate community and their effects on other population enhancing the contamination of environment

scholarly journals Real Time Air Quality Index from Various Locations

2020 ◽  
Vol 9 (2) ◽  
pp. 368-372
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Ground Level ◽  
Air Quality Index ◽  
Time Data ◽  
Open Government Data ◽  
Government Data

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

scholarly journals A Layered Recurrent Neural Network for Imputing Air Pollutants Missing Data and Prediction of NO 2, O 3, PM 10, and PM 2.5

2020 ◽  
Author(s):  
Hamza Turabieh ◽  
Alaa Sheta ◽  
Malik Braik ◽  
Elvira Kovač-Andrić
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Air Quality ◽  
Recurrent Neural Network ◽  
Missing Values ◽  
Prediction Models ◽  
Air Pollutant ◽  
Forecasting Models ◽  
Monitoring Strategies ◽  
Artificial Neural Network Ann

To fulfill the national air quality standards, many countries have created emissions monitoring strategies on air quality. Nowadays, policymakers and air quality executives depend on scientific computation and prediction models to monitor that cause air pollution, especially in industrial cities. Air pollution is considered one of the primary problems that could cause many human health problems such as asthma, damage to lungs, and even death. In this study, we present investigated development forecasting models for air pollutant attributes including Particulate Matters (PM2.5, PM10), ground-level Ozone (O3), and Nitrogen Oxides (NO2). The dataset used was collected from Dubrovnik city, which is located in the east of Croatia. The collected data has missing values. Therefore, we suggested the use of a Layered Recurrent Neural Network (L-RNN) to impute the missing value(s) of air pollutant attributes then build forecasting models. We adopted four regression models to forecast air pollutant attributes, which are: Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Artificial Neural Network (ANN) and L-RNN. The obtained results show that the proposed method enhances the overall performance of other forecasting models.

scholarly journals EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

2017 ◽  
Vol 10 (9) ◽  
pp. 3255-3276 ◽  
Author(s):  
Augustin Colette ◽  
Camilla Andersson ◽  
Astrid Manders ◽  
Kathleen Mar ◽  
Mihaela Mircea ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Boundary Conditions ◽  
Radiative Forcing ◽  
Regional Scale ◽  
Air Pollutant ◽  
Pollutant Emissions ◽  
Mitigation Measures ◽  
Modelling Experiment ◽  
Technical Features

Abstract. The EURODELTA-Trends multi-model chemistry-transport experiment has been designed to facilitate a better understanding of the evolution of air pollution and its drivers for the period 1990–2010 in Europe. The main objective of the experiment is to assess the efficiency of air pollutant emissions mitigation measures in improving regional-scale air quality. The present paper formulates the main scientific questions and policy issues being addressed by the EURODELTA-Trends modelling experiment with an emphasis on how the design and technical features of the modelling experiment answer these questions. The experiment is designed in three tiers, with increasing degrees of computational demand in order to facilitate the participation of as many modelling teams as possible. The basic experiment consists of simulations for the years 1990, 2000, and 2010. Sensitivity analysis for the same three years using various combinations of (i) anthropogenic emissions, (ii) chemical boundary conditions, and (iii) meteorology complements it. The most demanding tier consists of two complete time series from 1990 to 2010, simulated using either time-varying emissions for corresponding years or constant emissions. Eight chemistry-transport models have contributed with calculation results to at least one experiment tier, and five models have – to date – completed the full set of simulations (and 21-year trend calculations have been performed by four models). The modelling results are publicly available for further use by the scientific community. The main expected outcomes are (i) an evaluation of the models' performances for the three reference years, (ii) an evaluation of the skill of the models in capturing observed air pollution trends for the 1990–2010 time period, (iii) attribution analyses of the respective role of driving factors (e.g. emissions, boundary conditions, meteorology), (iv) a dataset based on a multi-model approach, to provide more robust model results for use in impact studies related to human health, ecosystem, and radiative forcing.

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

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
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.

scholarly journals A Novel Air Quality Early-Warning System Based on Artificial Intelligence

2019 ◽  
Vol 16 (19) ◽  
pp. 3505
Author(s):  
Mo ◽  
Zhang ◽  
Li ◽  
Qu
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Prediction Model ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Air Pollutant ◽  
Concentration Data ◽  
Whale Optimization ◽  
The World

The problem of air pollution is a persistent issue for mankind and becoming increasingly serious in recent years, which has drawn worldwide attention. Establishing a scientific and effective air quality early-warning system is really significant and important. Regretfully, previous research didn’t thoroughly explore not only air pollutant prediction but also air quality evaluation, and relevant research work is still scarce, especially in China. Therefore, a novel air quality early-warning system composed of prediction and evaluation was developed in this study. Firstly, the advanced data preprocessing technology Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) combined with the powerful swarm intelligence algorithm Whale Optimization Algorithm (WOA) and the efficient artificial neural network Extreme Learning Machine (ELM) formed the prediction model. Then the predictive results were further analyzed by the method of fuzzy comprehensive evaluation, which offered intuitive air quality information and corresponding measures. The proposed system was tested in the Jing-Jin-Ji region of China, a representative research area in the world, and the daily concentration data of six main air pollutants in Beijing, Tianjin, and Shijiazhuang for two years were used to validate the accuracy and efficiency. The results show that the prediction model is superior to other benchmark models in pollutant concentration prediction and the evaluation model is satisfactory in air quality level reporting compared with the actual status. Therefore, the proposed system is believed to play an important role in air pollution control and smart city construction all over the world in the future.

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