Air Pollution and Traffic Congestion in Urban India

Cities of Dragons and Elephants ◽

10.1093/oso/9780198829225.003.0019 ◽

2019 ◽

pp. 606-638

Author(s):

Surender Kumar

Keyword(s):

Air Pollution ◽

Traffic Congestion ◽

Strong Predictor ◽

Urban Air Pollution ◽

Small Scale ◽

Urban Air ◽

Predictor Of Success ◽

Increasing Demand ◽

Small Scale Industries ◽

Critical Overview

This chapter provides a critical overview of urban air pollution and traffic congestion in India. It discusses temporal and spatial variations in the level of urban air pollution and traffic congestion in the country and challenges posed by them. India loses about 2 per cent of its GDP because of urban air pollution. Urbanization of small-scale industries and increasing demand for private vehicles are major determinants of urban air pollution and traffic congestion. Legitimacy in enforcement of polices is a strong predictor of success of a policy. Growing demand for green cities coupled with transparency in governance will reduce frictions arising from pollution and congestion.

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Do Rail Transits Improve Local Air Quality? Take Chengdu-Nanchang for Example

10.21203/rs.3.rs-320209/v1 ◽

2021 ◽

Author(s):

Xueyuan Wang ◽

Yuping Wang ◽

Zhijian Zhang ◽

Jingwei Li

Keyword(s):

Air Pollution ◽

Carbon Monoxide ◽

Air Quality ◽

Traffic Congestion ◽

Road Traffic ◽

Urban Air Pollution ◽

Point Estimate ◽

Urban Air ◽

Rail Transit ◽

Transit System

Abstract Many cities in China have invested the city’s rail transit system to reduce urban air pollution and traffic congestion. Earlier studies rarely compare the effects of rail transit on urban air quality in different cities, providing little guidance to urban planners in solving traffic congestion and air quality. By using the rail transit lines in Chengdu and Nanchang as case studies, this paper attempts to examine the effect of rail transit on air pollution. Data were collected from 18 monitoring stations distributed along the chosen rail transit lines in both cities during the period 2014 to 2016 and analyzed using the regression discontinuity design to address the potential endogenous location of subway stations. The results show that subway opening in Nanchang has a better reductions from automobile exhaust than that in Chengdu, specifically, carbon monoxide pollution, one key tailpipe pollutant, experienced a 10.23% greater reduction after Nanchang Metro Line 1 opened. On the contrary, the point estimate for carbon monoxide in Chengdu is 22.42% and statistically significant at the 1% level. Nanchang Metro Line 1 does play an important role in road traffic externalities, but the benefit was not huge enough to change the overall air quality. On the contrary, the opening of the Chengdu Metro Line 4 is unlikely to yield improvements in air quality.

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An improved, practical, computerized atmospheric dispersion model for calculating urban air pollution concentrations

10.2514/6.1977-74 ◽

1977 ◽

Author(s):

D. CHOU ◽

J.-Y. SUNG

Keyword(s):

Air Pollution ◽

Dispersion Model ◽

Urban Air Pollution ◽

Atmospheric Dispersion ◽

Urban Air ◽

Atmospheric Dispersion Model ◽

Pollution Concentrations

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An Urban Air Pollution Early Warning System Based on PM2.5 Prediction Applied in Ploiesti City

Revista de Chimie ◽

10.37358/rc.17.4.5566 ◽

2017 ◽

Vol 68 (4) ◽

pp. 858-863

Author(s):

Mihaela Oprea ◽

Marius Olteanu ◽

Radu Teodor Ianache

Keyword(s):

Air Pollution ◽

Early Warning ◽

Urban Areas ◽

Early Warning System ◽

Urban Air Pollution ◽

Warning System ◽

Air Pollutant ◽

Pollution Level ◽

Urban Air ◽

Prediction Approach

Fine particulate matter with a diameter less than 2.5 �m (i.e. PM2.5) is an air pollutant of special concern for urban areas due to its potential significant negative effects on human health, especially on children and elderly people. In order to reduce these effects, new tools based on PM2.5 monitoring infrastructures tailored to specific urban regions are needed by the local and regional environmental management systems for the provision of an expert support to decision makers in air quality planning for cities and also, to inform in real time the vulnerable population when PM2.5 related air pollution episodes occur. The paper focuses on urban air pollution early warning based on PM2.5 prediction. It describes the methodology used, the prediction approach, and the experimental system developed under the ROKIDAIR project for the analysis of PM2.5 air pollution level, health impact assessment and early warning of sensitive people in the Ploiesti city. The PM2.5 concentration evolution prediction is correlated with PM2.5 air pollution and health effects analysis, and the final result is processed by the ROKIDAIR Early Warning System (EWS) and sent as a message to the affected population via email or SMS. ROKIDAIR EWS is included in the ROKIDAIR decision support system.

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