Urban air quality management plan (UAQMP) is an effective and efficient tool employed in managing acceptable urban air quality. However, the UAQM practices are specific to a country’s needs and requirements. Majority of the developed countries have full-fledged UAQMP with a regulatory management framework. However, developing countries are still working in formulating the effective and efficient UAQMPs to manage their deteriorating urban air environment. The first step in the process of formulation of UAQMP is to identify the air quality control regions based on ambient air quality status and second, initiate a time bound program involving all stakeholders to develop UAQMPs. The successful implementation of UAQMPs depends on the strength of its key components, e.g. goal/objective, monitoring network, emission inventory, air quality modeling, control strategies and public participation. This paper presents a comprehensive review on UAQMPs, being implemented worldwide at different scales e.g., national (macro), city (medium), and local (micro). © Author(s) 2015. This work is distributed under the Creative Commons Attribution 3.0 License.

S. M. Shiva Nagendra

Department Of Civil Engineering

Fulltext

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Urban air quality management-A review

Atmospheric Pollution Research

One of the major drawbacks of conventional air quality models is their inability in accurately predicting extreme air pollutant concentrations. Hybrid modelling is one of the techniques that estimates/predicts the ‘entire range’ of the distribution of pollutant concentrations by combining the deterministic based models (capable in predicting average range) with suitable statistical (probability) distribution models (capable in predicting extreme range). This research paper describes system based approach in developing hybrid model to predict hourly averages as well as extreme percentile ranges of NOx and PM2.5 concentrations at two urban locations having complex traffic heterogeneity, highly variable tropical meteorology and different geographical characteristics. At one of the selected locations i.e. Delhi megacity, during winters, hybridization of AERMOD and Lognormal predicts NOx and PM2.5 concentrations satisfactorily with index of agreement ‘d’ values of 0.98–0.99, respectively; however, during summers, AERMOD-Log-logistic and AERMOD-Lognormal are best predicting NOx and PM2.5 concentrations with d values of 0.98–0.96, respectively. In another, i.e., Chennai, a coastal megacity, AERMOD-Lognormal predicts PM2.5 concentrations satisfactorily with d values of 0.98 and 0.99 during winter and summer seasons, respectively. Further, hybrid model has also been used to evaluate regulatory compliance. © 2017 Elsevier Ltd

Transportation Research Part D: Transport and Environment

A system based approach to develop hybrid model predicting extreme urban NOx and PM2.5 concentrations

Swelling urban populations and increased concentration of automobile traffic in and around cities have resulted in severe air pollution, threatening human health and undermining the productivity of the urban population. Air quality modelling is one of the important components of urban air quality management (UAQM). This paper contains comparative performance results of three most used commercial air quality models (AQMs) e.g. AERMOD, ADMS-urban and ISCST3. The models are evaluated based on their predictive accuracy of 1-h average PM2.5 concentrations at selected air quality control regions (AQCRs) in the Delhi and Chennai cities in India and Newcastle city in the United Kingdom for critical winter period of year 2009. At Delhi AQCR, ISCST3 predictions of PM2.5 concentrations are more accurate (d = 0.66) than AERMOD (d = 0.51) and ADMS-Urban (d = 0.52). Similar performance of the models have been observed at Chennai city AQCR, i.e., index of agreement ‘d’ = 0.52 for ISCST3 and d = 0.40 and 0.47 for AERMOD and ADMS-Urban, respectively. However, at Newcastle city AQCR, the ISCST3 has predicted PM2.5 concentrations with an accuracy having d = 0.56 while AERMOD and ADMS, having d = 0.53 and 0.54, respectively. Differences in ‘d’ values clearly indicate causal nature of these Gaussian based models and their sensitivity to input data as well as their parsimonies (less input data requirement by models and more accuracy in output results) to geographical and meteorological conditions. © 2015, Metrology Society of India.

Mapan - Journal of Metrology Society of India

Comparative Evaluation of Air Quality Dispersion Models for PM2.5 at Air Quality Control Regions in Indian and UK Cities

Urban air pollution poses a significant threat to human health, the environment and the quality of life of people throughout the world. In the United Kingdom 103 areas have been declared as local air quality management areas (LAQMA). While in India, 72 cities have been identified as cities having poor air quality/non-attainment area, i.e., the air quality in these cities are exceeding prescribed National Ambient Air Quality Standards (NAAQS). The transport sector is the principal source of local air pollution in urban areas, because of the increased vehicular population, vehicle kilometres travelled (VKT) and lack of infrastructure development. Many mathematical models have been widely used as tools in local air quality management in developed countries. Among them, ADMS [1] and AERMOD [2] models have been widely used for urban air quality management in Europe and the US, respectively. However, their applications are limited in developing countries like India due to the lack of readily available input data, time and the cost involved in collecting the required model input data. In this paper the performance evaluation of ADMS and AERMOD in predicting particulate matter (PM) concentrations at road sides in Chennai, India and Newcastle, UK is discussed. The statistical parameters such as Index of Agreement (IA), Fractional Bias (FB), Normalized Mean Square Error (NMSE), Geometric Mean Bias (MG) and Geometric Mean Variance (VG) have been used to evaluate the ADMS and AERMOD model performance. Results indicated that both the models are able to predict the pollutant concentrations with reasonable accuracy. The IA values for ADMS and AERMOD are found to be 0.39 and 0.37 and 0.48 and 0.44, respectively, for the Chennai and Newcastle study sites. © 2012 WIT Press.

WIT Transactions on Ecology and the Environment

Application of ADMS and AERMOD models to study the dispersion of vehicular pollutants in urban areas of India and the United Kingdom

In this paper, the chemical characterization of PM10 and PM2.5 mass concentrations emitted by heterogeneous traffic in Chennai city during monsoon, winter and summer seasons were analysed. The 24-h averages of PM10 and PM2.5 mass concentrations, showed higher concentrations during the winter season (PM10=98μg/m3; PM2.5=74μg/m3) followed by the monsoon (PM10=87μg/m3; PM2.5=56μg/m3) and summer (PM10=77μg/m3; PM2.5=67μg/m3) seasons. The assessment of 24-h average PM10 and PM2.5 concentrations was indicated as violation of the world health organization (WHO standard for PM10=50μg/m3 and PM2.5=25μg/m3) and Indian national ambient air quality standards (NAAQS for PM10=100μg/m3 and PM2.5=60μg/m3).The chemicals characterization of PM10 and PM2.5 samples (22 samples) for each season were made for water soluble ions using Ion Chromatography (IC) and trace metals by Inductively Coupled Plasma Optical Emission Spectroscopy (ICP-OES) instrument. Results showed the dominance of crustal elements (Ca, Mg, Al, Fe and K), followed by marine aerosols (Na and K) and trace elements (Zn, Ba, Be, Ca, Cd, Co, Cr, Cu, Mn, Ni, Pb, Se, Sr and Te) emitted from road traffic in both PM10 and PM2.5 mass. The ionic species concentration in PM10 and PM2.5 mass consists of 47-65% of anions and 35-53% of cations with dominance of SO42- ions. Comparison of the metallic and ionic species in PM10 and PM2.5 mass indicated the contributions from sea and crustal soil emissions to the coarse particles and traffic emissions to fine particles. © 2011 Elsevier B.V.

Science of the Total Environment

Chemical characterization of PM10 and PM2.5 mass concentrations emitted by heterogeneous traffic

Chemosphere

Effects of China’s current Air Pollution Prevention and Control Action Plan on air pollution patterns, health risks and mortalities in Beijing 2014–2018

Who's Who

Huey, Robert John, (born 9 April 1959), Chief Veterinary Officer for Northern Ireland and Deputy Secretary, Department of Agriculture, Environment and Rural Affairs, Northern Ireland, since 2013

Journal	Atmospheric Pollution Research
Publisher	Dokuz Eylul Universitesi
ISSN	13091042
Open Access	Yes