**9. Validation of NO<sup>x</sup> and PM<sup>10</sup>**

The annual PM<sup>10</sup> and NO<sup>x</sup> concentration contour plots for all sources of the study domain are shown in Figure 6a,b respectively. A comparison of simulated concentration using WRF output with the observed concentration of PM<sup>10</sup> and NO<sup>x</sup> are in Table 1, and the model was well-compared for this study area. The root mean square error and mean bias error between predicted and observed concentrations for NO<sup>x</sup> were 1.76 and 0.063, respectively, while they were 0.41 and 0.83 for PM10, respectively. The standard deviations for NO<sup>x</sup> at BPCL and HPCL were 33.6 and 30.2, respectively, and the standard deviations for PM<sup>10</sup> at BPCL and HPCL were 16.4 and 12.4, respectively. It was seen that the values obtained through air quality modeling were closer to the observed concentrations with the mesoscale meteorology than the surface level meteorology. The model results using observed surface meteorology were high. Modeled values were in good agreement with the observed values at both locations for NOx, but for PM10, simulated concentrations were lesser than the observed concentration at HPCL. This can be due to the vehicular congestion and resuspended particles. The model performed well with mesoscale meteorology after all the sources and the entire region were considered. As Chembur has immense variation in topography, land use and geographical structure, as shown in Figure 1d, microscale meteorology varies with these factors. Further, mesoscale meteorology has been used for other analyses for air quality. The contours were plotted using the model for NO<sup>x</sup> and PM<sup>10</sup> concentrations based on one-hour average values for one year for all sources in the study area. The same analysis was repeated only for industrial sources and vehicular emission sources separately. It was observed that the maximum concentration of PM<sup>10</sup> was 71.8 µg/m<sup>3</sup> .This concentration was observed near the Chembur Gaothan area where the vehicular congestion was more intense. Around BPCL and HPCL area, PM<sup>10</sup> concentration was around 50 µg/m<sup>3</sup> . The minimum concentration of PM<sup>10</sup> was less than 42 µg/m<sup>3</sup> , and this concentration was observed near the southern boundary of the study region, which has been represented by deep violet color. The PM<sup>10</sup> modeling was carried out using 30 µg/m<sup>3</sup> as the background concentration. This concentration was estimated when modeled concentrations matched with observed concentrations. This background concentration also includes resuspended particulate matter (RSPM), which is induced by vehicular congestion and other factors. In PM<sup>10</sup> modeling, this background concentration

can be taken as a correction factor. The maximum concentration of NO<sup>x</sup> was observed to be 53 µg/m<sup>3</sup> near the Ghatkopar–Mankhurd link road. This can be due to the vehicular congestion in Deonar Village, BPCL and HPCL area. Annual minimum concentration was less than 10 µg/m<sup>3</sup> along the eastern, western and southern boundaries of the study area, which has been shown in deep violet color.

**Figure 6.** Annual Concentration of (**a**) NO<sup>x</sup> and (**b**) PM<sup>10</sup> (in µg/m<sup>3</sup> ) using observed meteorology for Chembur Area.


**Table 1.** Monthly and Annual Comparison of Simulated Concentration with Ambient Observed Concentration (µg/m<sup>3</sup> ).


**Table 1.** *Cont*.

Emission load does not represent the rank-wise contribution of sources to the ambient concentration of the region. Hence, modeling was carried out for industrial sources, vehicle sources, and low duty diesel vehicles (LDDVs) to observe the relative sourcewise contribution to the ambient air quality for future scope of implementation of control strategies and environmental management.

#### *9.1. Results of Industrial Sources*

For industrial emission sources modeling, four industries (BPCL, HPCL, RCFL and TPCL) were considered in Chembur. In this study, NO<sup>x</sup> and PM<sup>10</sup> emissions were modeled for the year to find out the dominant source in the study domain.

### *9.2. Contribution of NO<sup>x</sup> and PM<sup>10</sup> Concentration by Industries*

NO<sup>x</sup> and PM<sup>10</sup> emission load were 64% and 94%, respectively from industries in Chembur (Figure 3). However, it contributes less to ambient concentration in the study area. The southern part of the study area is dominated by industrial sources, and due to meteorology, the maximum concentration of NO<sup>x</sup> is 6.2 µg/m<sup>3</sup> , seen at HPCL. Also, NO<sup>x</sup> concentration is 4.8 µg/m<sup>3</sup> in the southern part of BPCL. The maximum concentration of PM<sup>10</sup> is 35 µg/m<sup>3</sup> at BPCL and 33 to 34 µg/m<sup>3</sup> at HPCL and RCFL. Table 2 shows the comparison of the simulated concentration of NO<sup>x</sup> and PM<sup>10</sup> for industrial sources only and ambient simulated concentration of NO<sup>x</sup> and PM<sup>10</sup> for this study area, respectively.


**Table 2.** Comparison of simulated concentration from industries with ambient simulated concentration of NO<sup>x</sup> and PM10.

#### *9.3. Results of Line Sources*

In line source modeling, six roads in Chembur area have been considered. In the present study, NO<sup>x</sup> and PM<sup>10</sup> emissions have been modeled for the year to find out the dominant sources in the study domain. Vehicular emission varies with time such as morning peak, evening peak, off peak and the lean peak of the day.

### *9.4. NO<sup>x</sup> and PM<sup>10</sup> Concentration Contribution by Vehicles*

NO<sup>x</sup> and PM<sup>10</sup> emission loads from vehicles in Chembur are 17% and 3%, respectively (Figure 3). Nevertheless, these are contributing considerably to ambient concentration because they are ground emission sources. The northern part of the study area is dominated by vehicular sources and high density of vehicles. The maximum concentration of NO<sup>x</sup> was 43 µg/m<sup>3</sup> at Chedda Nagar and 40.1 µg/m<sup>3</sup> at Chembur Naka. The southern part of the study area has an inconsequential effect on vehicular pollution (only 2–5 µg/m<sup>3</sup> ). At Chheda Nagar, the concentration of ambient air quality from all the sources was 54 µg/m<sup>3</sup> , while the concentration by vehicular sources was 41 µg/m<sup>3</sup> . Chheda Nagar is in the northern region of the study area, and this part is affected less by industrial emissions. The northern part of the study domain is highly populated compared to the southern part. The southern part has lesser contribution from vehicles. At Chheda Nagar, the maximum concentration of PM<sup>10</sup> from vehicles was 37.84 µg/m<sup>3</sup> and from the other sources was 70.8 µg/m<sup>3</sup> .

## *9.5. NO<sup>x</sup> and PM<sup>10</sup> Concentration Modeling by Diesel Car and LDDV*

Northern and western corners in this analysis are dominated by diesel cars and light duty diesel vehicles (LDDVs), while the other areas are almost free from NO<sup>x</sup> pollutant. The maximum concentration of NO<sup>x</sup> was 12.5 µg/m<sup>3</sup> and it was found at Chheda Nagar. The entire area of Shramjivi Nagar showed NO<sup>x</sup> concentration of 7-9 µg/m<sup>3</sup> sourced from diesel cars and LDDVs. Concentration contribution in ambient air quality from vehicles was 43.1 µg/m<sup>3</sup> . Thus, it can be concluded that diesel cars and LDDVs are contributing to one fourth of the line source emission. In PM<sup>10</sup> concentration modeling, northern and western corners are affected by diesel cars and LDDVs. The maximum PM<sup>10</sup> concentration of 32.8 µg/m<sup>3</sup> was observed at Everard Nagar (Point 1 area) and 32.5 µg/m<sup>3</sup> at Chembur Naka from diesel cars and LDDVs with 30 µg/m<sup>3</sup> background concentration. Thus, it can be concluded that diesel cars and LDDVs are contributing to around 17% to line sources and 25% to the total concentration.

#### **10. Summary and Conclusions**

The aim of the study was to generate onsite meteorological data for usage in air quality modeling to see the feasibility of coarse resolution of WRF output in LMIC. It generated onsite and real time meteorological data, which was fed in AERMET, the pre-processor of the dispersion model AERMOD. AERMOD calculated concentrations for NO<sup>x</sup> and PM<sup>10</sup> using compiled emission inventory for all the sources, namely industries and vehicles, of the study area. Air quality modeling results showed that in this particular case, the meteorological data from WRF output at mesoscale performed better than the observed meteorological data. WRF output could be a good option which may represent a better meteorology for the purpose of dispersion modeling. Also, this may be because industrial sources have a significant contribution in the region. The results were used to identify the critical areas and relative contribution of various sources to ambient air quality. The use of WRF model is very economical in resources and time.

The ambient concentration load of the study area is shown in Figure 7 for NO<sup>x</sup> and PM10. Here, for ambient concentration, the vehicular emissions are dominating in the region because these are ground level sources. Based on the study, following conclusions can be drawn:

	- ➢ At ambient concentration of NOx, diesel cars and LDDVs contributed one fourth of the line sources for this study domain.

**Figure 7.** Comparison between emission load and ambient concentration load of NO<sup>x</sup> and PM<sup>10</sup> in Chembur area.

Micro-meteorology may vary a lot due to the topography of the region. Topographic features could be one of the limitations of the use of WRF output meteorology for air quality modeling because it may not capture the high-rise buildings in the region. In AERMOD, only one meteorological profile can be used but meteorology may not be uniform for the entire region. It has been found many times that the observed meteorological parameters carry some measurement errors [54–56].

This will be very useful in the forecast, implementation, control, and management strategies for improving air quality and also for performing a risk analysis of different types of sources in the region as future scope. Also, this work can be useful to regulatory authorities to develop a framework for air quality management in LMIC. The shortage of research was (a) the meteorological data were available at only one location which can be observed at other locations and comparison can be done for the same and (b) suspended dust can be estimated and incorporated in emission inventory. Various physics option parameters to set up the WRF model and simulation of the microscale meteorology with a comparison of observed meteorology could be possible future researches. Health benefit analysis can also be done by estimating population exposure with air quality [57].

**Author Contributions:** Conceptual, methodology development, software operation and writing, A.K., review, data procurement and guidance, A.K.D. and R.S.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Ethical review and approval are not required for the study as the research does not involve humans.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Data available on request.

**Conflicts of Interest:** The authors declare no conflict of interest.

## **References**

