DIURNAL VARIATIONS OF AIR POLLUTION FROM MOTOR VEHICLES IN RESIDENTIAL AREA

In Indian metropolitan cities, the extensive growth of the motor vehicles has resulted in the deterioration of environmental quality and human health. The concentrations of pollutants at major traffic areas are exceeding the permissible limits. Public are facing severe respiratory diseases and other deadly cardio-vascular diseases In India. Immediate needs for vehicular air pollution monitoring and control strategies for urban cities are necessary. Vehicular emission is the main source of deteriorating the ambient air quality of major Indian cities due to rapid urbanization. Total vehicular population is increased to 15 Lacks as per recorded data of Regional Transport Organization (RTO) till 2014-2015. This study is focused on the assessment of major air pollution parameters responsible for the air pollution due to vehicular emission. The major air pollutants responsible for air pollution due to vehicular emissions are PM10, PM2.5, Sox, Nox, HC, CO2 and CO and Other meterological parameters like Ambient temperature, Humidity, Wind direction and Wind Speed. Sampling and analysis of parameters is carried out according to National Ambient Air Quality Standards Guidelines (NAAQS) (2009) and IS 5128.

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Pengembangan Kebijakan Menekan Emisi Kendaraan Bermotor Di Indonesia

Warta Penelitian Perhubungan ◽

10.25104/warlit.v22i1.1001 ◽

2010 ◽

Vol 22 (1) ◽

pp. 98-111

Author(s):

Panal Sitorus

Keyword(s):

Public Health ◽

Air Pollution ◽

Information Systems ◽

Motor Vehicles ◽

Legal Action ◽

Big Cities ◽

Emissions Testing

Levels of air pollution caused by motor vehicles in the big cities in Indonesia is quite high, about 60%was alarming and impact on public health. The main problem is why should conduct an effectivetesting of emissions vehicles in Indonesia? Tire aim of study is to provide recommendations haw toreduce emissions levels, the object of studtj is the implementation of emissions testing, characteristicsand facilities. The method used is to compare with the threshold determined btJ the data from variousofficial. sources.Tire success of reducing emissions levels is in the supervision of testing and legal action in its testingsystem.Need to reform its institutions emissions testing, technician certification, the appointment of calibrationinstitution, improving information systems, certification, and socialization to the communihJcontinuously.Keyword: Emissions and public health.

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Air pollution from motor vehicles: issues and options for Latin American countries

The Science of The Total Environment ◽

10.1016/0048-9697(95)04662-k ◽

1995 ◽

Vol 169 (1-3) ◽

pp. 303-310 ◽

Cited By ~ 13

Author(s):

Asif Faiz ◽

Surhid Gautam ◽

Emaad Burki

Keyword(s):

Air Pollution ◽

Latin American ◽

Motor Vehicles ◽

Latin American Countries

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Deep Learning-based Framework for Smart Sustainable Cities

International Journal of Intelligent Information Technologies ◽

10.4018/ijiit.2019100105 ◽

2019 ◽

Vol 15 (4) ◽

pp. 76-107

Author(s):

Nagarathna Ravi ◽

Vimala Rani P ◽

Rajesh Alias Harinarayan R ◽

Mercy Shalinie S ◽

Karthick Seshadri ◽

...

Keyword(s):

Neural Network ◽

Air Pollution ◽

Deep Learning ◽

Air Pollutants ◽

Human Life ◽

Deep Belief Network ◽

Motor Vehicles ◽

Human Beings ◽

Long Term Effects ◽

Belief Network

Pure air is vital for sustaining human life. Air pollution causes long-term effects on people. There is an urgent need for protecting people from its profound effects. In general, people are unaware of the levels to which they are exposed to air pollutants. Vehicles, burning various kinds of waste, and industrial gases are the top three onset agents of air pollution. Of these three top agents, human beings are exposed frequently to the pollutants due to motor vehicles. To aid in protecting people from vehicular air pollutants, this article proposes a framework that utilizes deep learning models. The framework utilizes a deep belief network to predict the levels of air pollutants along the paths people travel and also a comparison with the predictions made by a feed forward neural network and an extreme learning machine. When evaluating the deep belief neural network for the case study undertaken, a deep belief network was able to achieve a higher index of agreement and lower RMSE values.

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The use of mathematical modeling methods for solving geo-ecological problems of sustainable development of regions

E3S Web of Conferences ◽

10.1051/e3sconf/202016101005 ◽

2020 ◽

Vol 161 ◽

pp. 01005

Author(s):

M V Volik

Keyword(s):

Mathematical Modeling ◽

Air Pollution ◽

Sustainable Development ◽

Numerical Study ◽

Digital Technologies ◽

Motor Vehicles ◽

Gaseous Pollutants ◽

Mathematical Apparatus ◽

Modeling Methods ◽

Ecological Problems

Currently, a number of environmental problems have a significant impact on the stable economic development of the country. A global problem is the study of air pollution. The solution of such geoecological problems should be carried out with the use of modern mathematical apparatus and digital technologies. The paper presents the results of a numerical study of the distribution of gaseous pollutants emitted by motor vehicles in the pedestrian zone of streets. It is shown that the vortex structures formed in the studied city buildings development have a significant impact on the accumulation of anthropogenic impurities.

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Fine-Grained Spatiotemporal Analysis of the Impact of Restricting Factories, Motor Vehicles, and Fireworks on Air Pollution

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph17134828 ◽

2020 ◽

Vol 17 (13) ◽

pp. 4828

Author(s):

Mei Yang ◽

Hong Fan ◽

Kang Zhao

Keyword(s):

Air Pollution ◽

Air Quality ◽

Spatiotemporal Analysis ◽

Yangtze River Delta ◽

Influential Factors ◽

Motor Vehicles ◽

Emission Scenarios ◽

Fine Grained ◽

The Impact ◽

Pm2.5 Concentration

Aiming at improving the air quality and protecting public health, policies such as restricting factories, motor vehicles, and fireworks have been widely implemented. However, fine-grained spatiotemporal analysis of these policies’ effectiveness is lacking. This paper collected the hourly meteorological and PM2.5 data for three typical emission scenarios in Hubei, Beijing–Tianjin–Hebei (BTH), and Yangtze River Delta (YRD). Then, this study simulated the PM2.5 concentration under the same meteorological conditions and different emission scenarios based on a reliable hourly spatiotemporal random forest model ( R 2 exceeded 0.84). Finally, we investigated the fine-grained spatiotemporal impact of restricting factories, vehicles, and fireworks on PM2.5 concentrations from the perspective of hours, days, regions, and land uses, excluding meteorological interference. On average, restricting factories and vehicles reduced the PM2.5 concentration at 02:00, 08:00, 14:00, and 20:00 by 18.57, 16.22, 25.00, and 19.07 μ g / m 3 , respectively. Spatially, it had the highest and quickest impact on Hubei, with a 27.05 μ g / m 3 decrease of PM2.5 concentration and 17 day lag to begin to show significant decline. This was followed by YRD, which experienced a 23.52 μ g / m 3 decrease on average and a 23 day lag. BTH was the least susceptible; the PM2.5 concentration decreased by only 8.2 μ g / m 3 . In addition, influenced by intensive human activities, the cultivated, urban, and rural lands experienced a larger decrease in PM2.5 concentration. These empirical results revealed that restricting factories, vehicles, and fireworks is effective in alleviating air pollution and the effect showed significant spatiotemporal heterogeneity. The policymakers should further investigate influential factors of hourly PM2.5 concentrations, combining with local geographical and social environment, and implement more effective and targeted policies to improve local air quality, especially for BTH and the air quality at morning and night.

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Traffic-Related Airborne VOC Profiles Variation on Road Sites and Residential Area within a Microscale in Urban Area in Southern Taiwan

Atmosphere ◽

10.3390/atmos11091015 ◽

2020 ◽

Vol 11 (9) ◽

pp. 1015

Author(s):

Jiun-Horng Tsai ◽

Yen-Ting Lu ◽

I-I Chung ◽

Hung-Lung Chiang

Keyword(s):

Urban Area ◽

Ambient Air ◽

Abundant Species ◽

Residential Area ◽

Motor Vehicles ◽

Strategy Development ◽

Residential Areas ◽

Volatile Organic ◽

Methyl Cyclopentane ◽

Set Up

The sampling sites, including roadsides and residential areas, were set up to collect ambient air and determine the volatile organic species it contained. For the roadside air, the average VOCs (volatile organic compounds) abundant at rush hour periods was two times that at non-rush hour periods. In the residential area, the VOC concentrationswere106 and 129 ppb during rush hour periods. The VOC concentration ratios of roadside and residential areas were in the range of 1.08–1.75 and the traffic emissions were related to the VOCs abundant in air. The highest VOC concentration was 168 ppb at midnight at residential sites and the VOC abundance could be two times that of roadside sites. This level of concentration could be attributed to the application of solvents and to human activity in a nearby motorcycle/vehicle maintenance plant, laundry rooms, etc. High abundant species were similar in both the roadside and residential air samples. These highly abundant species included toluene, acetone, acetonitrile, m,p-xylene and n-pentane, all of which can be emitted from traffic exhaust. Benzene, acrolein, formaldehyde, vinyl chloride and 1,3-butadiene were the main species with health impacts collected at both sites. In the micro-scale environment, the residential ambient air was affected by traffic flow from morning to night. In the midnight period, some local activities (a motorcycle/vehicle maintenance shop and laundry shops) affected the concentrations of certain VOCs (acetonitrile, toluene, hexane, 2-methylpentane, methyl cyclopentane and 3-methylpentane). The traffic and motor vehicles’ effects were determined, which could be useful for air quality management and strategy development in an urban area.

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The Efficiency of Economic Performance, Electricity Consumption, and Environmental Pollutants in Taiwan

Mathematical Problems in Engineering ◽

10.1155/2020/8260965 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Wen-jie Zou ◽

Tai-Yu Lin ◽

Yung-ho Chiu ◽

Ting Teng ◽

Kuei Ying Huang

Keyword(s):

Air Pollution ◽

Economic Development ◽

Air Pollutants ◽

Electricity Consumption ◽

Living Environment ◽

Air Pollutant ◽

Disposable Income ◽

Pollutant Emissions ◽

Motor Vehicles ◽

Overall Efficiency

Finding the balance between economic development and environmental protection is a major problem for many countries around the world. Air pollution caused by economic growth has caused serious damage to humans’ living environment, and as improving energy and resource efficiencies is the first priority, many countries are targeting to move towards a sustainable environment and economic development. This study uses the modified dynamic SBM (slack-based measure) model to explore the economic efficiency and air pollutants emission efficiency in Taiwan’s counties and cities from 2012 to 2015 by taking labor, motor vehicles, and electricity consumption as inputs and average disposable income as output. Particulate matter (PM2.5), nitrogen oxide emissions (NO2), and sulfur oxide emissions (SO2) are undesirable outputs, whereas factory fixed assets are a carry-over variable, and the results show the following: (1) the regions with the best overall efficiency between 2012 and 2015 include Taipei City, Keelung City, Hsinchu City, Chiayi City, and Taitung County; (2) in counties and cities with poor overall efficiency performance, the average disposable income per household has no significant relationship with air pollutant emissions; (3) in counties and cities where overall efficiency is poor, the average efficiency of each household’s disposable income is small; and (4) except for the five counties and cities with the best overall performance, the three air pollutants in the other fourteen counties and cities are high. Overall, the air pollution of most areas needs improvement.

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