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Urban Roadside Monitoring, Modeling and Mapping of : A case study of New ,

A. Masooda,*, A. Kafeela, A. Shamshada aDepartment of Civil Engineering, F/O Engineering & Technology, Jamia Millia Islamia, 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 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.

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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 . 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

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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 traffic variations for 8h survey period on Mathura Road

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Table 1: Percentage share of vehicles on the road based on vintage [18]

Age of vehicles Percentage Share of Vehicles on Road (years)

2W (%) 3W (%) Cars (%) HV (%) LCV (%) 15-20 - - 5 - - 10-15 20 - 10 20 20 5-10 30 50 35 30 40 0-5 50 50 50 50 40

Table 2: Number of Vehicle belonging to different vehicle classes estimated on the basis of their percentage share on the road during peak hours (0900 hrs-1000 hours)

Average Number of vehicles age CAR LCV 2W 3W HV 15-20 289 - - - - 10-15 578 22 850 - 177 5-10 2025 43 1275 489 266 0-5 2892 43 2125 489 443

2.3 Meteorological data Onsite meteorological data such as wind speed, wind direction, temperature, relative humidity and average sunshine hours was collected for the month of November, 2011 from CRRI. The average wind speed was observed as 3.1m/s with a prominent N-W direction. Calm wind conditions (<1ms-1) were observed almost 10% of the time [19]. The average sunshine hours were recorded as 10.54 hours. Based on this meteorological information the stability classes were elucidated using Pasquill-Gifford stability classification. All meteorological data was recorded and processed so that it may work as an input for CALINE4 model.

2.4 Emission monitoring data Hourly average CO concentration data was acquired from CRRI for the month of November, 2011 at Mathura Road. The location of the monitoring station has been shown in Fig. 1.

2.5 CALINE4 Model description CALINE4 model is the last in the series of the line source air quality model established by California Department of Transportation (CALTRANS). To represent pollutant dispersion over roadway the model is based on the Gaussian diffusion equation, engaging a mixing zone concept [9, 38, 42]. In order to compute the air quality impact of traffic emissions, the model incorporates information on vehicle emissions, traffic characteristics and meteorological conditions[37, 39]. In representing a road segment the CALINE4 model employs a series of identical finite line sources (FLS).The total road network is broken down into a finite number of elements and each element is modeled, analogous to a finite line source located normal to the wind direction and centered at element midpoint [40][41]. For each element, a local x-y coordinate system aligned with wind direction and originating at mid point is defined. Emissions appearing within an element are considered to be discharged along a finite line source and

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follow Gaussian dispersion pattern downwind of the element [20]. For a distinct receptor position, the total concentration can be assessed by computing and summing up the incremental concentration from each link. The product of central sub elevation line source strength (QE) and a weighting factor (WT) will decide the source strength for each segment. FLS contribution for a maximum of 6 segments is calculated by the model within ±3σy of the receptor [21]. The concentration at a receptor from an infinitesimal FLS segment dy can be computed using the following equation:

(1) dc is the incremental concentration of CO ( g/m3), q is the lineal source strength (grams), u is the wind -1 speed (ms ), H is the source height (m), are horizontal and vertical dispersion parameters, z is the vertical distance(m) and y is the crosswind distance(m) [22, 9].

2.6 Modeling Domain Air quality modeling was executed in a domain of 500mx400m, spreading longitudinally and laterally around the Mathura road. For predicting the air quality impact the entire study domain was broken down into 222 grids of 30mx30m respectively. The receptors were positioned at each node of the grid to facilitate the coverage of all pertinent locations in and around the Mathura road region. The receptor locations in a grid pattern have been produced by ArcGIS and are shown on a Cartosat satellite image in Fig. 3.

Fig.3. The map showing the distribution of 30m x 30m grid on the Cartosat satellite image

2.7 Model inputs 2.7.1 Emission factors

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Emission factors may be defined as a value that represents the relation between pollutants released in the atmosphere and the activity associated with the release of the pollutant [33]. In our study emissions along with acceptable deterioration factors were considered. Emission factors were acquired from a limited study conducted by Indian Institute of petroleum, Council of Scientific and Industrial Research (CSIR). CO Emission factors in g km-1 for different vehicles classes have been represented in Table 3:

Table 3: Emission factors for different vehicle classes in accordance to their vintage

CO Emission factors for different vehicle classes according to vehicle Age of vehicles vintage in g km-1 2W 3W LCV Cars/jeep HV 15 to 20 - - - 9.8 - 10 to 15 4 - 6.9 3.9 4.5 5 to 10 2.2 0.1 5.1 1.98 3.6 0 to 5 1.4 0.1 0.72 1.39 3.2

Eventually, the composite emission factors were calculated on an hourly basis for a period of 8 hours i.e. from 0800-1600 hours for five different vehicle classes using the following equation:

(2)

Where V1, V2, V3, V4 represent the number of vehicles belonging to a particular class between the age group 15-20, 10-15, 5-10,0-5 years; DF1, DF2, DF3, DF4 represent the deterioration factor for every vehicle age group; EF1, EF2, EF3, EF4 are the corresponding emission factors for different vehicle classes in accordance to their vintage as mentioned in Table 3. These average weighted emission factors listed in Table 4 have been increased (~10%) to account for the vehicles operating under cold-start conditions [31, 32, 9].

Table 4: Average weighted CO emission factors for 8h duration

Duration Average weighted CO emission factor in (g/mile/vehicle) (hours)

0800-0900 44.3 0900-1000 43.2 1000-1100 44.8 1100-1200 45.8 1200-1300 46.3 1300-1400 46.0 1400-1500 46.4 1500-1600 46.3

2.7.2 Road link data A single Road link was adopted for our study selected out from the Mathura road segment having endpoint coordinates as (721800mE, 316000mN) and (721800mE, 3160050mN) in Universal Transverse

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Mercator (UTM) coordinate system. The Width of the link was considered as 35m which includes two traffic lanes (11m and 15m respectively), 2.5 m footpath and 4 m divider. The link height was considered as 0. Traffic volume (vehicles/hour) was computed on the basis of the hourly traffic survey data for the month of November 2011, and composite emission factors (g/mile) listed in Table 4 were utilized as the road link data.

2.8 Model Run CALINE4 model was run in two different modes; the standard run and the multi-run for predicting hourly average and 8 hourly average CO concentrations at the receptor locations. For each run, the model processes twenty receptor coordinates depicting the receptor locations that were predetermined before the modeling episode. Emission factors listed in Table 4 were inputted cumulatively or separately for multi run and standard run conditions. The predicted emissions from the model run correspond to the emission factors applied [2].Various input parameters for the CALINE4 model run have been listed in Table 5

Table 5: CALINE 4 run condition parameters adopted in the study Parameters value Remarks Wind speed(m/s) 3 Monitored wind speed (CRRI) Wind direction(degrees) 270 Wind blowing towards west ( 0° is north) Standard deviation(degrees) 17.5 Can be taken up to 20 degrees [17] Mixing height(m) 500 Height at which thermal turbulence occur due to solar heating of ground Ambient temperature(°C) 21 Ambient average temperature of November 2011 Ambient pollutant concentration(ppm) 5 Preexisting background concentration (CRRI)

2.9 Geo statistical Analysis - Inverse Distance Weighted Inverse Distance Weighted (IDW) interpolation deduces cell values utilizing a linearly weighted cluster of training points [23]. IDW is established on intuitively appealing conception that values close to data points have more relevance as compared to distant observations [46]. The affect of any observation is considered to be inversely linked to its distance from a foreign location [24, 47, 48]. In order to estimate a value from an unmeasured location, IDW engages the measured values which surround the prediction location [50]. IDW assigns more weightage to the points closer to the prediction location as compared to those which are distant [49]. IDW being deterministic does not consider the spatial arrangement of the training points. Due to this reason, IDW results may be affected by sample spacing and density [25]. IDW can be computed using the following equations[50]:

(3)

(4)

Where means unknown pollutant concentration data, means pollutant concentration data value at known receptors, N represents the number of receptors, wi represents the weighting of each receptor, di means the distance from each receptor to the unknown location and is the control parameter or the power [26].

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3.0. Results and Discussion 3.1 Comparative analysis of predicted and observed CO concentrations Average hourly CO concentrations and 8 hourly average concentrations were determined by CALINE4 model. These predictions were calculated using the standard run mode and multi run mode of the CALINE4. The background concentration was taken as 5ppm for the entire modeling episode. As shown in Fig. 4 the predicted concentrations were compared with the actual monitored concentrations at the location (722350mN, 3160100mE). It can be seen in Fig. 4 that the predicted concentrations curve remains downgraded vis-à-vis monitored concentrations curve however both the curves exhibit a fair degree of agreement between them. The validity of the above situation has been somewhat supported by Goyal and Krishna (1999) who reported that the CALINE 4 model under predicts in crosswind cases and over predicts in parallel wind cases [27]. For the entire duration, the predicted concentrations transcended the NAAQS for CO. The 95% confidence limits have also been reckoned for both the predicted and the monitored data values and consequently the upper and the lower confidence limits were determined.

Fig.4. Comparison of actual and predicted CO concentrations at the location (722350mN, 3160100mE).

In the case of monitored values, the upper confidence limit was calculated as 6680 µg/m³ and the lower as 6509µg/m³ respectively. Similarly for predicted concentration data the upper limit was computed as 6351µg/m³ and lower limit as 6243µg/m³.

3.2 Statistical analysis for performance evaluation In order to assess the model’s performance, statistical analysis was carried out which involved the determination of RMSE (Root mean square error) and correlation coefficient (r). The RMSE was computed using the following equation [28]:

(5)

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Where, RMSE is the root mean square error of the model n is the number of accuracy assessment points

is the concentration predicted by CALINE4

is the monitored concentration In our case, the RMSE value in µg/m³ was determined as 302(0.25 in ppm) for hourly average CO concentrations at the monitoring station location (722350mN, 3160100mE) during an 8 hour period (0800-1600hours). The predicted and the monitored data were further correlated as shown in the Fig.5. The coefficient of correlation (r) was determined as 0.87 which signifies a strong correlation and infers that CALINE4 model is able to describe 87% of the model variations [29]. This reflects towards the reasonable performance shown by CALINE4 model under given traffic, meteorological and terrain conditions.

Fig.5. Correlation between predicted and observed CO concentrations at the monitoring station location (722350mN, 3160100mE).

3.3 Spatial patterns of predicted hourly and 8 hourly CO concentrations In order to generate maps, the predicted CO concentrations were processed with spatial analyst tool IDW. A total of 252 grid receptor locations (Cartesian coordinates) were utilized by IDW interpolation method for representing pollutant’s concentration spread over the study area. The whole area was divided into a 30mx30m grid to facilitate the map preparation process. The maps represented in Figures 6(a-i) were generated for hourly and 8 hourly simulated CO concentrations from (0800-1600 hours). A high density of emissions can be noticed in the 1st and 2nd hour maps (0800-1000 hours) which represent the peak traffic hours. It is observed that the CO concentrations decline as the lateral distance from the roadway increases. The value range decreases with the increase in distance from the centre [35]. The period from 5th hour to 8th hour exhibits shrinkage of vulnerability zones in the map. These zones are characterized by high concentration values and indicate pollution Hot spots [30]. The main hotspots or the areas of maximum concentrations lie on the Mathura road manifested as green dots spread around the center of the map. As the concentration spreads it dilutes and reaches a minimal value around the outskirts of Mathura road. The reason behind this concentration build up at the centre is

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because of the traffic flow, fairly low vehicular speeds and traffic congestion. Similar trends can be seen for every hour with CO concentrations decreasing drastically at the road outskirts and patches of elevated concentration levels being observed at the centre.

a

1st hour mapping of CO concentration b

2nd hour mapping of CO concentration

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c

3rd hour mapping of CO concentration d

4th hour mapping of CO concentration

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e

5th hour mapping of CO concentration

f

6th hour mapping of CO concentration

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g

7th hour mapping of CO concentration h

8th hour mapping of CO concentration

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i

8 hour cumulative mapping of CO concentration

Fig. 6. (a)-(h) Hourly CO concentration maps using IDW from 0800 hours to 1600 hours ;(i) 8 Hour cumulative CO concentration map using IDW.

4. Conclusions

Mathura road has become the center of attraction for many researchers and scientists who have been carrying out air quality studies. The road experiences heavy traffic throughout the day and has become a high pollution zone in the capital city. Our study demonstrates the simulation performance and the evaluation of the line source air quality model CALINE4 at the busiest urban highway corridor in Delhi. In our results, it has been observed that the monitored and the predicted data sets exhibit a correlation for the given time frame of the research. The predicted concentration remains lower compared to the monitored concentration, asserting the under prediction of the CALINE4 model. In order to assess the model’s performance RMSE method was conducted by applying simulated and monitored CO concentrations at the (722350mN, 3160100mE) receptor location. Low RMSE value of 0.25 and r value of 0.87 indicate a satisfactory CALINE4 model performance. Integration of emission data with GIS displays an effective method for execution of more complex environmental analysis that fosters decision making process of urban planning. Moreover, spatial distribution can assist in marking out the pollution hotspots in that area. In this study, highly visual vulnerability maps highlight the Hot Spots around the Mathura Road region. From the maps, it can be perceived that a concentration value of 6250-6875 µg/m³ holds up a considerable share and the higher values are confined to the Road region due to the unprecedented level of vehicles. Considering the heterogeneous nature of the traffic conditions, these maps play a decisive role in upgrading the environmental management around the Mathura Road. The study is thus an attempt to visually describe the effect of urban traffic characteristics on environment and thereby providing assertions in order to improve the air quality near the Mathura road region. Moreover the study bestows understanding to policymakers and planers about the varied consequences of planning alternatives for our site and for this purpose the role of interactive software like GIS would be potent.

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