Nepal Ambient Monitoring and Source Testing Experiment (NAMaSTE): emissions of particulate matter and sulfur dioxide from vehicles and brick kilns and their impacts on air quality in the Kathmandu Val

4.1

 Meteorology

The different emissions scenarios did not impact simulated meteorological conditions. Therefore, we only present the statistical analysis of our HTAP_vehicle_brick simulation, using the HTAP inventory with updated emissions for both vehicles and brick kilns. Model-simulated 2 m temperature and relative humidity, as well as 10 m wind speed, are compared to observations at two sites in the Kathmandu Valley: Bode and the Tribhuvan International Airport. Figure 4 shows the comparisons of predicted daily-averaged quantities with observations and Table 4 presents the statistical indices of comparisons for each site. The temperature at Bode is simulated with a correlation of 0.8 and a small negative NMB of 4.7 %. At the airport, the correlation of 0.7 is close to that at Bode, but a larger bias is observed, with NMB of 15.8 %. The model systematically underestimates relative humidity with a correlation of 0.5–0.6 and NMB of −40.8 % to −34.0 %, due to an underestimation of both minima and maxima. In a previous WRF-Chem study in the Kathmandu Valley (Mues et al., 2018), an underestimation of relative humidity was also clearly observed near the ground. The temporal correlation coefficient of daily 10 m wind speed is 0.7 at the airport and 0.8 at Bode. Although the model reproduces the daily variability well, it overestimates the wind speed at both sites. The model performs better at Bode (NMB = 67 %) than at the airport site (NMB = 176 %) in simulating wind speed. The modeled mean wind speed at the airport site is about 1.68 m s−1 higher than the observation, with a RMSE of 1.74 m s−1. WRF-Chem usually has difficulty simulating wind speed over complex mountain terrains; a larger bias over mountain regions was also found in previous studies (Mar et al., 2016; Mues et al., 2018). Zhang et al. (2013) explained that the overestimation in wind speeds is likely caused by poor representation of surface drag exerted by unresolved topographical features in WRF-Chem. A closer look at the wind observational data reveals the presence of a local, thermally driven diurnal wind circulation that controls the airflow regime in the Kathmandu Valley during weak gradient synoptic-scale flow and arguably significantly impacts the air quality in the valley (Fig. 5). During the day, the winds at both Bode and the airport are predominantly from the SW quadrant along the axis of the nearby river, with hourly-averaged magnitudes of up to 5 m s−1. In contrast, the katabatic winds during the night are much weaker, generally under 1 m s−1 and with a prevailing easterly component. Note that winds at Bode are consistently stronger than those at the airport. One of the most likely reasons for that is the differing measurement height at both sites: 23 m above surface at Bode and 10 m at the airport.

Download Print Version | Download XLSX

Download

The model generally reproduces the wind direction shift at both sites quite well (Figs. 6 and 7); however, the wind speed magnitude is substantially overpredicted. The overestimation is not that large during the day but it is severe during the nighttime hours, suggesting serious differences in the structure of simulated and observed nighttime boundary layers. We should note here that the model does not directly predict the wind field at 10 m height; instead it is extrapolated from the first model level using Monin–Obukhov similarity theory (Jimenez et al., 2012), and therefore is highly influenced by the PBL scheme used in the simulations.

Download

Download

Download

4.2

 AOD

AOD is a column-integrated measurement and, thus, not directly correlated with concentrations of near-surface PM. However, in the context of model validation, AOD is useful as a general indicator of near-surface air quality. Figure 8 depicts the spatial pattern of the 2-week mean AOD observed by MODIS and the modeled AOD at the 550 nm wavelength. In general, the simulated AOD values are higher in the southern part of the domain than those in the northern part. MODIS AOD also shows a similar spatial distribution in the southern part but most data in the northern part are missing due to cloud coverage. It is clear from the figure that adding brick kiln emissions has little impact on AOD in the Kathmandu Valley, while modifying vehicle emissions leads to a significant increase in modeled AOD. The average difference in AOD between the simulations with and without the revised vehicle emissions is 18 % in the Kathmandu Valley. As discussed in Sect. 3.2, diesel engines emit a large amount of EC. These aerosols strongly absorb sunlight at all UV–vis wavelengths and, consequently, contribute to higher AOD values.

Download

The time series of simulated versus observed daily mean AOD at the two AERONET sites within the model domain (Jomsom, Nepal and QOMS_CAS, China) and the corresponding performance statistics are presented in Fig. 9. The model tends to overestimate the lower observed values at the QOMS_CAS site, with an MFB of 56 %. At Jomsom, the model predicts the lower measured AOD values reasonably well during 15–24 April. However, the model misses the peak on 14 April, when the observed AOD is near 1.0, an indication of severe air pollution. Instead, our model predicts a somewhat lower peak on the preceding day. This high AOD value was driven by emissions from a wildfire located southeast of Jomsom. It can be seen clearly from the model simulation (Fig. S1) that the fire caused high surface PM and CO concentrations near the burning area on 12 April. Since the prevailing wind direction on 13 April in the simulation was from the southeast and with the overestimated wind speed, the smoke was transported to the northwest and increased the simulated AOD value at the Jomsom site earlier than observed.

Download

4.3

 EC

Figure 10 shows the spatial pattern of simulated EC, averaged during the simulation period. The average simulated EC concentration in the Kathmandu Valley during the 2-week simulation period was approximately 6.2 µg m−3, higher than concentrations in the surrounding regions. Vehicles (primarily diesel trucks and buses) contribute approximate 85 % of total EC emissions, whereas brick kilns account for only about 0.11 %. Consequently, the simulation that includes brick kiln emissions (HTAP_vehicle_brick) does not improve the model performance in predicting EC at Bode relative to that with updated vehicle emissions (HTAP_vehicle).

Download

Figure 11 depicts the time series of observed and simulated surface EC concentrations. The average EC concentration observed at Bode during the campaign was 5.6 µg m−3 during daytime, 10.82 µg m−3 during nighttime, and 8.32 µg m−3 for the 24 h average. Two factors contributed to the nighttime increase in surface concentrations: (1) the diminishing mixing-layer depth and (2) the air flow circulation shift in response to surface cooling. As the night progresses, the turbulent mixing in the developing nocturnal boundary layer is suppressed and air pollutants are confined in a shallow layer close to surface. The shift in the wind direction is also conducive to increased surface concentrations as the Bode site is located downwind with respect to the major cluster of brick kilns in the area (Figs. 2 and 5). In contrast, during daytime the mixing-layer depth increases in response to solar heating and promotes the vertical mixing throughout the depth of the whole boundary layer, leading to a notable decrease in surface concentrations. The Bode site during the day is also upwind from the brick kiln cluster, which leads to further reduced concentrations. This idealized scenario holds true for most of the simulation period with the exception of the days between 13 and 16 April, when the local air flow circulation is disrupted by a large-scale disturbance. During that period, the wind speeds are generally below 2 m s−1 even during the day, suggesting that the peak in the surface concentrations (Fig. 11) could be related to suppressed boundary-layer mixing.

Download

Observed EC concentrations are strongly underestimated with poor correlation when the original HTAP emissions are used as model input (MFB = −125 %, r=0.19 for 24 h averaged EC, Table 5). This is similar to Mues et al. (2018), who reported an underestimation of EC concentrations by a factor of 5 when using HTAP emissions in their simulations. The simulation with updated vehicle emissions (HTAP_vehicle) shows reduced bias and better correlation (MFB = −73 %, r=0.61 for 24 h average), although the model still underestimates EC concentrations.

Download Print Version | Download XLSX

The model captures the daytime low concentrations very well during 18–21 April, but it fails to predict the observed peak on 15–16 April. Such high EC concentration episodes can be caused by either stagnant meteorological conditions or enhanced emissions. We examined the precipitation and wind speed at Bode during the high and low episode periods (Table S2). On 16 (high EC) and 19 April (low EC), no rainfall was observed. The daytime wind speed on 16 April (2.4 m s−1) was slightly lower than that on 19 April (2.7 m−1) but the EC concentration on 16 April was 6 times higher. This suggests that a sporadic emissions source (e.g., garbage or biomass burning) that was not accounted for in our model could be responsible for the observed high concentration episode.

The nighttime surface concentrations are significantly underestimated by the model throughout the entire period. One possible cause for this underestimation is illustrated in Fig. S2, showing the diurnal evolution of the modeled mixing layer height. During the night, the simulated boundary layer remains well mixed up to a height of 500 m, which facilitates the vertical transport of pollutants, and consequently, leads to lower surface concentrations.

Download

It is also possible that we might have underestimated the EC emissions from the brick kilns near Bode. In our emissions inventory we use an average kiln productivity to estimate the emissions; thus if the productivities of these kilns are higher than the average, or if their efficiency is lower, their emissions could have been underestimated in the inventory. Another possibility is that additional sources are still missing, for example, garbage burning, biomass burning, and/or diesel generators. Stockwell et al. (2016) found that garbage burning in the Kathmandu Valley may produce significantly more EC emissions than previously thought. Due to power outages, especially in the dry season, the use of generators was still prevalent in the valley in 2015. A study conducted by the World Bank (2014) found that nearly 200 000 small power generators, powered by diesel, were used for pervasive power shortages in Nepal. Nevertheless, these comparisons suggest that there is still a need for further improvement of constructing local emissions inventories in the Kathmandu Valley.

4.4

SO2

Figure 10 presents the 2-week average SO2 concentrations for the three simulations. The average SO2 concentration in the HTAP_vehicle_brick simulation is approximately 3.6 µg m−3 in the Kathmandu Valley, higher than the surrounding areas in Nepal. Revised vehicle emissions have little impact on SO2 concentration in the valley, but brick kiln emissions contribute 50 % of simulated SO2 concentrations. The 2-week mean SO2 concentration measured at the Bode site was 39.7 µg m−3. The model largely underestimates the observation, with the modeled mean SO2 concentration of 5.3 µg m−3. SO2 is mainly a primary pollutant, directly emitted from sources. SO2 concentrations are highly related to its emissions. The large discrepancy between the model simulation and observation at this site is probably because our model resolution is not able to capture spatially concentrated high emissions of SO2 near Bode. Kiros et al. (2016) measured SO2 concentrations at 15 sites in 8 weeks from March to May 2013 in the Kathmandu Valley and observed high SO2 concentrations close to brick kilns. In particular, the average SO2 concentration measured at Bode was the highest (39.2 µg m−3) and 2–6 times higher than those at the other 14 sites (4.7–15.8 µg m−3) in the Kathmandu Valley. They explained that the elevated surface SO2 concentration at Bode is mainly caused by nearby brick kilns, which are fueled by coal. We found that there are 12 brick kilns located within the 4 km distance from the Bode site. Although we have included emissions of these brick kilns in our model inputs, we may still have overestimated dilution and/or underestimated emissions from these brick kilns by using monthly average productivity and average emissions factors of zigzag brick kilns. We hope to improve our emissions inventory of brick kilns when more information on individual brick kilns becomes available.

Figure 12 compares modeled 2-week mean SO2 concentrations with observed 8-week mean SO2 measured between 23 March and 18 May 2013, reported in the study of Kiros et al. (2016). None of these sites exceeded the Nepal national air quality standard of 70 µg m−3 for the 24 h mean, but SO2 concentrations at the Bode site were almost twice as high as the WHO standard of 20 µg m−3. Since our own NAMaSTE campaign only collected SO2 at the Bode site, we also included the study of Kiros et al. (2016) to illustrate the magnitude difference in observational data at different locations within the Kathmandu Valley. The 2-week mean SO2 concentration from NAMaSTE in 2015 was 39.7 µg m−3 at Bode, while the 8-week mean in 2013 by Kiros et al. (2016) was 39.2 µg m−3, showing similarities, giving us confidence that comparing the magnitude difference among sites was possible, despite the difference in observed years. We used these 2013 measurements to represent the ambient SO2 concentrations during our simulation period. Including brick kiln emissions improves model prediction of SO2 concentrations at all sites. The simulated SO2 concentrations with brick kiln emissions are closer to observations compared with those without them, although the model still underestimates SO2. This underestimation is probably due to brick kiln SO2 emissions. We applied an emissions factor of 12.7 g kg−1 of fuel measured from zigzag kilns (Stockwell et al., 2016) to all types of brick kilns. These were the only available observational data in Nepal at the time of this study. A more recent study by Nepal et al. (2019) reported that the mean value of the SO2 emissions factor from zigzag kilns is 24 ± 22 g kg−1 of fuel, which is almost twice as high as that used in our study. If we doubled our SO2 emissions for brick kilns, the modeled SO2 concentrations would be much closer to the observations. Assuming the linear relationship in SO2, the average difference between the observed and modeled SO2 concentrations would drop from 4.4 to 2.8 µg m−3. We plan to revisit our brick kiln emissions inventory as more emissions factors become available. Our study highlights the importance of improving the emissions factor of SO2 for brick kilns in Nepal. The difference between observation and model simulation ranges from 0.8 to 10 µg m−3 for all sites except Bode where the difference is 34.4 µg m−3. This result suggests that surface SO2 concentrations in the Kathmandu Valley are highly variable and are influenced by nearby sources. Future simulations with a higher spatial model resolution and emissions inputs may help to resolve the strong spatial gradients in SO2 concentrations.