Masood & al. / Appl. J. Envir. Eng. Sci. 3 N°2(2017) 179-194 Urban Roadside Monitoring, Modeling and Mapping of Air Pollution: A case study of New Delhi, India A. Masooda,*, A. Kafeela, A. Shamshada aDepartment of Civil Engineering, F/O Engineering & Technology, Jamia Millia Islamia, New Delhi 110025, India *Corresponding Author, Email ID: [email protected] Received 24 Apr2017, Revised 13 Jun 2017, Accepted 20 Jun 2017 Abstract Our study in its primal stage focused on modeling CO emissions emanating from Mathura road, with the use of CALINE4 model. For the execution of modeling process a set of emission factors employing simplified vehicle classification methodology were utilized. The predicted CO concentrations at the receptor location (722350mN, 3160100mE) were compared with actual monitored concentrations and a fair agreement between the two data sets was observed. 95% confidence interval was deduced for both monitored and predicted concentrations. The upper and lower confidence limits for predicted concentration were determined as 6351µg/m³ and 6243µg/m³ and for the monitored concentration the values were obtained as 6680µg/m³and 6509µg/m³. Moreover CALINE4 model’s performance was evaluated by determining root mean square error (RMSE) in µg/ m³ as 302 and coefficient of correlation (r) as 0.87. The study in its terminal stage involved integration of predicted CO emissions with ArcGIS enabling GIS to post process the data in the form of digital maps. These maps highlighted the hot spots around our site which may enable planners and policy makers to formulate better pollution control strategies, lay down stringent air quality standards and address the major environmental impact assessment factors. Keywords: Emission factor, Air pollution modeling, CALINE4, RMSE, GIS, IDW. 1. Introduction Delhi has become the South Asian economic hub and its economy has shown a humongous growth over the last decade. With a population of 16.9 million spread across 1483 km2of land (2007-2008), the city has experienced annual population growth rate of 1.92% over the last decade (2001-2011)[43,44]. Transport network in Delhi is primarily road based with 2103 km of road per 100km2 of area. The road lengths have extended from being 14316 km in 1980-81 to 31969 km in 2011-12. Due to growing urbanization, living standards in Delhi have changed drastically resulting in a higher sale of cars, especially diesel run cars. In the last couple of years, diesel run cars have seen a prodigious increase from 18 to 62% which has resulted in elevated pollution levels in Delhi [1]. Only the CO levels in the city have shown a descent over the years due to the conversions of 3wheelers to CNG [2]. It is estimated that around 3000 metric tons of pollutants are emitted by vehicles plying on the road which may be a result due to the fact that 30% of the time vehicles travel at speed lower than 20km/hr [3,4]. Over the last two decades, Petrol and diesel consumption have escalated to a whopping 400% and 300% respectively [2]. Almost 67% of the total air pollution in Delhi results due to vehicular emissions, coal based thermal power plants provide a mere 13% contribution followed by a 12% contribution by industrial units and remaining 8% via domestic activities [3,36]. Expeditious increase in vehicular population over the last few years has played a consequential role in deteriorating Delhi’s air quality. 179 Masood & al. / Appl. J. Envir. Eng. Sci. 3 N°2(2017) 179-194 Private vehicles in Delhi have shown an annual average growth rate of 7.4% whereas commercial vehicles have shown a growth rate of 9.15 % [5, 34]. Unplanned urban and industrial developments, high influx of population and elevated consumption trends have been highly influential in deteriorating Delhi’s air quality [6, 45]. The registered vehicular population has shown a 92% hike over the last two decades. Thus the situation calls for the use of tools and techniques which may assist in establishing a thorough air quality management plan. Air quality models are those tools which figure out the associated potential impacts from the existing sources and also assist in framing control strategies for the air pollution episodes [7, 8]. CALINE4 is a line source Gaussian dispersion model that quantifies the effect on air quality by assimilating information on vehicle emissions, traffic characteristics and meteorological conditions [9]. The model predicts concentration for NOx, CO and particulates. This predicted CO concentration integrates with ArcGIS and are applied as spatial information to prepare CO concentration maps. GIS is an effective tool for mapping pollutants and providing a comprehensive visual representation of the impact on the environment by an air pollution episode. Research studies have been executed in the past which have coupled air quality models with GIS to define the air pollution episode of a certain area. Rebolj and Peter, (1999) provided a synopsis of the basic road traffic emission model and then targeted the design and execution of the computer application with prominence on the used component and GIS technology [10]. Gulliver and Briggs (2004) presented their research describing the development and testing of a GIS-based system for modeling human journey-time exposures to traffic-related air pollution: STEMS (Space–Time Exposure Modeling System) [11]. Their model integrated data on source activity, pollutant dispersion, and travel behavior to derive individual or group-level exposure, measures to atmospheric pollution .Bachman et al. (2000) discussed how GIS - based modeling approach called Mobile emission assessment system for urban and regional evaluation (MEASURE) provides researchers and planners with means of assessing motor vehicle emission reduction strategies[12]. Guttikunda et al., (2013) utilized a GIS based spatial inventory which was coupled with the temporal resolution of 1hour, for chemical transport modeling using the ATMoS dispersion model [13]. Jensen et al., (2001) developed a new prototype model named Air GIS which predicted air pollution levels at high temporal and spatial resolutions, their model enabled them to map traffic emissions, air quality levels, and human exposure levels at residents’ addresses and at workplaces [14]. Their prototype was a GIS based component-oriented integrated system for estimation, visualization and analysis of road traffic air pollution. Another case study on Prague’s environment discussed the GIS based approach towards spatiotemporal analysis of environmental pollution in urban areas [15]. Lin and Lin (2002) carried out their air quality analysis research in Taichung city; they presented a preliminary study for the evaluation of transport- related air pollution situations in the area [16]. In the context of all these studies our research focuses on engaging CALINE4 dispersion model to estimate CO emissions under mixed traffic conditions on one of the busiest highway road corridors in Delhi i.e. the Mathura road. These predictions were compared with monitored data acquired from the CRRI monitoring station. Moreover, these predicted Concentrations were incorporated with a spatial database (ArcGIS) to illustrate air pollution Hot Spots in a 2-d GIS environment. Unlike other researches established on the air pollution scenario of this region, our research attempts to put forward a comprehensive, analytical and visual perspective of the state of air pollution in this area. The proposed research amplifies our knowledge to comprehend the air pollution developments in an urban roadside and also engages GIS to present an initial impression of the air quality. 2. Material and methods 2.1 Site Description For the present study, a segment of Mathura road was selected abutting the Central road research institute (CRRI) (28°33’3.9” N latitude and 77°16’27.9’’E longitude). The research site stretches 500 m longitudinally along the length and spreads 400 m laterally on either side of the road segment. The road 180 Masood & al. / Appl. J. Envir. Eng. Sci. 3 N°2(2017) 179-194 segment extends from Sukhdev Vihar Bus Depot to Okhla Sewage Treatment plant. A base map of the site showing the location of Mathura road and the monitoring station is shown in Fig.1. The road segment has been surrounded with a fair amount of green cover. With very few residential and institutional complexes located within 50 meters of the segment, the street canyon effect for this reason was neglected in our research [17]. The stretch experiences heavy traffic especially during the peak hours and over the years, traffic volume on this road has escalated to large proportions. Fig.1. Base map of the site showing the monitoring station location 2.2 Traffic data The traffic count exercise was executed on an hourly basis in the month of November, 2011. The survey commenced from 0800 hours in the morning and continued till 1600 hours in the evening. The entire traffic fleet was assorted in 5 classes viz, 2W (two wheelers), 3W (three wheelers: auto rickshaw), cars/jeep, HV (Heavy vehicles) and LCV (light commercial vehicles) and hourly traffic count were maintained. It was observed that cars/jeep dominated the traffic count data (43%) followed by 2W (36%), HV (11%), 3W (8%) and LCV (2%).The Vintage of vehicle data was procured from CRRI. The data was ascertained from the fuel station surveys, outdoor cordon surveys, fuel filling, garaging and registration patterns. Composite emission factor, an input for CALINE4 was worked out availing the traffic count data. Fig.2. – Hourly