Comment on essd-2022-162

This paper presents a bottom-up methodology to estimate multi-pollutant hourly gridded on-road traffic emission using advanced traffic flow and speed data for Delhi. We have used the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. At first the traffic volume and congestion (travel time delay) relation is applied to model the 24-hour traffic speed and flow for all the major road links of Delhi. The modelled traffic flow and speed shows an anti-correlation behaviour having peak traffic and emissions in morning-evening rush hours. We estimated an annual emission of 1.82 Gg for PME (Exhaust particulate matter), 0.94 Gg for BC (Black Carbon), 0.75 Gg for OM (Organic matter), 221 Gg for CO (Carbon monoxide), 56 Gg for NOx (Oxide of Nitrogen), 64 Gg for VOC (Volatile Organic Carbon), 0.28 Gg for NH3 (Ammonia), 0.26 Gg for N2O (Nitrous Oxide) and 11.38 Gg for CH4 (Methane) for 2018. The hourly emission variation shows bimodal peaks corresponding to morning and evening rush hours and congestion. The minimum emission rates are estimated in the early morning hours whereas the maximum emissions occurred during the evening hours. Inner Delhi is found to have higher emission flux because of higher road density and relatively lower average speed. Petrol vehicles dominate emission share (> 50 %) across all pollutants except PME, BC and NOx, and within them the 2W (Two-wheeler motorcycles) are the major contributors. Diesel fuelled vehicles contribute most of the PME emission. Diesel and CNG vehicles have a substantial contribution in NOx emission. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. Moreover, the developed methodology is a step forward in developing real-time emission with the growing availability of real-time traffic data. The complete dataset is publicly available on Zenodo at https://doi.org/10.5281/zenodo.6553770 (Singh et al., 2022).

Road traffic emission is considered to be the major source of pollution exposure in megacities around the globe. Traffic emission makes urban air pollution to be highly spatially heterogeneous with sharp concentration gradients that can vary substantially within a few meters near the road. The spatially heterogeneous and temporally varying emissions are required to account for  concentration gradients that have a direct impact on the population exposure to outdoor air pollution. However, estimating such a detailed emission is very complex as it requires multi-category emission factors and a huge amount of georeferenced detailed traffic activity data such as traffic volume and speed, distance traveled, vehicle category share, fuel share, engine share, technology share etc. In the absence of detailed data, emission estimations have been limited to coarser resolution which may not be suitable for high resolution air quality modeling, exposure assessment and management..Here we present an emission model to estimate multi-pollutant hourly gridded on-road traffic emission over Delhi. The model uses the globally adopted COPERT (Computer Programme to Calculate Emissions from Road Transport) emission functions to calculate the emission as a function of speed for 127 vehicle categories. For traffic activity, the emission model uses advanced traffic volume and speed data for Delhi obtained from TRIPP (Transportation Research and Injury Prevention Programme, IIT Delhi). Further the model considers the congestion (travel time delay based on TOMTOM) and speed-volume relation for different road categories to estimate hourly traffic volume and speed for each road link in Delhi that is used to calculate the hourly emissions using the COPERT emission functions Further, the emissions are gridded at 100 m × 100 m resolution to generate high-resolution spatio-temporal emission maps for Delhi and shown in Fig. 1 for four different hours of the day.We analyzed the modeled emissions to identify peak emission hours, pollution hotspots and most polluting vehicles. The hourly variation of emissions show distinct bimodal distribution with morning and dominant evening peaks for almost all pollutants linked with congestion and peak traffic.Figure 1. Estimated gridded NOx emission at 100m × 100m spatial resolution at different time of the day; the time is displayed in the upper-right corner of each subplot.The emissions are high near the busy roads and traffic junctions. The emission flux in the central areas of Delhi (Fig. 1) is 40-50% higher than mean emission flux due to the higher road and traffic density and lower average speed. Diesel vehicles have been found to be the dominant contributor to PM, BC and NOx emission. Our results suggest that the top 5 polluting vehicle categories account for more than half (55% - 91%) of the emissions. This study provides very detailed spatio-temporal emission maps for megacity Delhi, which can be used in air quality models for developing suitable strategies to reduce the traffic related pollution. The developed model can be applied for developing emission inventory and real-time emission with the growing availability of real-time traffic data.

Description of outdoor air quality in a typical residential area in Kuwait

In this study, black carbon (BC), particulate matter (PM), nitrogen oxides (NOx), and sulphur dioxide (SO2) were monitored between May 2019 and February 2020. During this sampling period, size-segregated particle samples were collected by a high-volume cascade impactor, once a week. Elemental carbon (EC) and organic carbon (OC) were investigated on the size-segregated particles. The study was carried out at Esenler district of Istanbul. Main sources in the vicinity of the sampling area are Central Coach Station of Istanbul, residential buildings, small-scale industrial facilities, and O-1 and O-2 highway connection. The average BC concentration was 2763 ± 1974 ng/m3. PM10 and PM2.5 concentrations were 38.2 ± 17.4 and 21.5 ± 12.4 μg/m3, respectively. BC comprised 12.8% and 7.2% of PM2.5 and PM10, respectively. Diurnal variation of BC had a similar trend with NO2 concentrations, which showed peak concentrations during rush hours. BC had high Pearson correlation coefficients with NO, NO2, and PM2.5. The geographical position of possible emission sources was estimated through conditional probability function. The Central Coach Station of Istanbul had the highest geographical possibility for BC sources. EC and OC exhibited bimodal size distribution. The coarse mode peak was at 0.95–1.5-μm size bin for OC, whilst it was at 1.5–3-μm size bin for EC. Since EC and BC exhibit the same origin with different measurement principles, we concluded that resuspension of road dust also contributed ambient BC concentrations.

An investigation of air quality from Eleme junction to Rumuokoro axis of Port-Harcourt, Rivers State, Nigeria has been conducted. This was due to the predominance of air pollutants from traffic congestion and industrialization in the study area. Eleme junction, Rumuodara junction and Rumuokoro junction are situated along the PortHarcourt East/West expressway, major route to other states of the federation and were selected because of the high traffic density usually observed at these junctions. Six criteria pollutants, namely, ground level ozone, carbon monoxide, sulphur dioxide, nitrogen oxide, volatile organic compound and particulate matter were measured respectively, during morning and evening hours; and their mean variation computed and compared with air quality index (AQI) and their limit of exceedance determined and categorized. The MultiRAE Seriesdetector and the X-AM-5000/5600 multi-gas detector were employed for the survey. The AQI varied between 246µg/m3 and 257.5µg/m3 in the morning hours; and 205.8µg/m3 and 313.7µg/m3 in the evening hours. These results exceeded the EPA air quality permissible limit, thus considered unhealthy for dwellers in those regions. Recommendations were proffered.

Road transport is one of the fastest-growing sectors contributing significantly to air pollution and greenhouse gas (GHG) emissions, especially in urban areas where exceedances of nitrogen oxides (NOx) and particulate matters (PM) values are frequent. These pollutants have clear health implications and indicate the need to plan and implement effective measures (such as introducing electric vehicles) to reduce road vehicles’ emissions. Emissions estimation from road transport could be applied using an emissions model. However, the emissions model reflecting Singapore’s local conditions is not yet available, and there are only limited emissions quantification efforts over the last few decades in Singapore. This research aims to analyse how and to what extent the existing emissions models apply to Singapore’s urban area emissions estimation. This aim is accompanied by vehicle fleet development and emissions inventory estimation for 2004-2019, the identification of aggregated emission factors (EFs) from air quality measurements at the Kallang Paya-Lebar (KPE) tunnel expressway and estimation of potential emissions reduction of electric vehicles (EV) scenario (up to 2050). The emissions loads for all pollutants showed a decreasing trend from 2004-2019. This result comes from the series of transportation policies such as vehicle growth control by way of vehicle quota system (VQS) and vehicle ownership in terms of the certificate of entitlement (COE) (which results in a high turnover rate of vehicles) and a combination of fiscal measures (e.g., vehicle taxes). A clear reduction trend was found for pollutants of carbon monoxide (CO) and volatile organic compounds (VOC), with passenger cars (PCs) and motorcycles (MCs) being the primary sources. Meanwhile, NOx, PM10 and PM2.5 emissions, which are mainly released by diesel vehicles, gradually decreased despite increasing the vehicle population using diesel and its transport activities. A comparison of bottom-up and top-down carbon dioxide (CO2) estimations showed less agreement, with a difference of 20% due to some limitations. The methodology, results and conclusions may apply to neighbouring cities in South-East Asia. The aggregated EF was derived from the Kallang-Paya Lebar (KPE) tunnel measurement in 2015 showed CO=1.46 g/veh-km and NOx=0.28 g/veh-km. These values are compared to previous studies performed in other tunnels globally. It was evident that both CO and NOx EFs are at a low-level range. Still, some parts need to be improved in future EFs development in a tunnel and other real-world measurements. Potential air pollutants and emissions reduction by penetration of electric vehicles (EVs) are estimated using COPERT emissions model under four scenarios; baseline, the basic scenario (S1), the medium scenario (S2) and the most ambitious scenarios (S3). Significant emissions reduction potential for 2050 is identified in scenario 3, with a reduction of 83%, 96%, and 85% in CO, VOC and CO2. NOx reduction is estimated at 66%, while for PM, the reduction is predicted to have less reduction (<50%) due to the still high share of non-exhaust emissions. The results provide a basis and support for additional policies to promote and manage EVs. Besides, insights into improving air quality are offered to support the global climate change issue.

The urban transport sector is one of most significant contributors to greenhouse gas (GHG) and air pollutant (AP) emissions. To achieve co-benefits of GHG and AP emission reductions, a synergistic mitigation approach targeting both climate change and air pollution has gained more attention. In this study, we evaluate mitigation synergy and policy implications for GHGs and nine APs, namely, sulfur dioxide (SO2), nitrogen oxides (NO x ), carbon monoxide (CO), particulate matters (PM10 and PM2.5), black carbon (BC), organic carbon (OC), volatile organic compounds (VOCs) and ammonia (NH3), in the transport sector of Xiamen, China, during the 2013–2060 period using the Low Emissions Analysis Platform model and quantitative analysis methods. Results show that light-duty vehicles, river boats, buses and heavy-duty trucks are significant common sources of GHG and AP emissions. Road sector abatement during 2013–2020 was most prominent, especially for CO, NO X , VOCs and GHGs. In this sector, guide green travel (GGT) and adjust energy structure (AES) are dominant measures for mitigation synergy between GHGs and APs. From 2021 to 2060, emission pathways for GHGs, SO2, CO, VOCs and NH3 under optimize transport structure (OTS), AES and GGT scenarios will decrease markedly. Their emissions will peak soon relative to those under business as usual scenario. Additionally, the potential of mitigation synergy may mainly be attributed to the road and shipping sectors under AES scenario, which is the most effective in reducing PM10, PM2.5, BC and OC emissions; the mitigation potential under the AES scenario for GHGs and other APs is nearly 1–4 times as high as that under OTS and GGT scenarios. Therefore, mitigation synergy, especially in adjusting the energy structure for the transport sector, is essential for achieving the simultaneous goals of the ‘blue sky’ and ‘carbon peaking and neutrality’.

Effects of fuel properties on exhaust emissions from diesel engines

In this study, we have assessed the three-dimensional (3-D) spatial extent of near-road air pollution around a signalized intersection in a densely populated area using collaborating methodologies of stationary measurements, drone monitoring, and atmospheric dispersion modeling. Stationary measurement data collected in the roadside apartment building showed a substantial effect of emitted pollutants, such as nitrogen oxides (NOx), black carbon (BC), and ultrafine particles (UFPs), especially during the morning rush hours. Vertical drone monitoring near the road intersection exhibited a steeper decreasing trend with increasing altitude for BC concentration rather than for fine particulate matter (PM2.5) concentration below the apartment building height. Atmospheric NOx dispersion was simulated using the weather research and forecasting (WRF) and computational fluid dynamics (CFD) models for the drone measurement periods. Based on the agreement between the measured BC and simulated NOx concentrations, we concluded that the air pollution around the road intersection has adverse effects on the health of residents living within the 3-D spatial extent within at least 120 m horizontally and a half of building height vertically during the morning rush hours. The comparability between drone monitoring and WRF-CFD modeling can further guarantee the identification of air pollution hotspots using the methods.

Evaluation of aircraft emission at Tribhuvan international airport and its contribution to air quality in Kathmandu, Nepal

Abstract. In this study 1 year of ceilometer measurements taken in the Kathmandu Valley, Nepal, in the framework of the SusKat project (A Sustainable Atmosphere for the Kathmandu Valley) were analysed to investigate the diurnal variation of the mixing layer height (MLH) and its dependency on the meteorological conditions. In addition, the impact of the MLH on the temporal variation and the magnitude of the measured black carbon concentrations are analysed for each season. Based on the assumption that black carbon aerosols are vertically well mixed within the mixing layer and the finding that the mixing layer varies only little during night time and morning hours, black carbon emission fluxes are estimated for these hours and per month. Even though this method is relatively simple, it can give an observationally based first estimate of the black carbon emissions in this region, especially illuminating the seasonal cycle of the emission fluxes. The monthly minimum median MLH values typically range between 150 and 200 m during night and early morning hours, the monthly maximum median values between 625 m in July and 1460 m in March. Seasonal differences are not only found in the absolute MLHs, but also in the duration of the typical daytime maximum ranging between 2 and 3 h in January and 6–7 h in May. During the monsoon season a diurnal cycle has been observed with the smallest amplitude (typically between 400 and 500 m), with the lowest daytime mixing height of all seasons (maximum monthly median values typically between 600 and 800 m), and also the highest night-time and early morning mixing height of all seasons (minimum monthly median values typically between 200 and 220 m). These characteristics can mainly be explained with the frequently present clouds and the associated reduction in incoming solar radiation and outgoing longwave radiation. In general, the black carbon concentrations show a clear anticorrelation with MLH measurements, although this relation is less pronounced in the monsoon season. The daily evolution of the black carbon diurnal cycle differs between the seasons, partly due to the different meteorological conditions including the MLH. Other important reasons are the different main emission sources and their diurnal variations in the individual seasons. The estimation of the black carbon emission flux for the morning hours show a clear seasonal cycle with maximum values in December to April. Compared to the emission flux values provided by different emission databases for this region, the estimated values here are considerably higher. Several possible sources of uncertainty are considered, and even the absolute lower bound of the emissions based on our methodology is higher than in most emissions datasets, providing strong evidence that the black carbon emissions for this region have likely been underestimated in modelling studies thus far.

Vehicular emissions in China in 2006 and 2010

Sources and radiative effects of wintertime black carbon aerosols in an urban atmosphere in east India

The tropospheric NO2 concentration from OMI AURA always shows high concentrations of NO2 at a few locations in India, one of the high concentrations of NO2 hotspots is associated with the locations of seven coal-fired Thermal Power plants (TPPs) in Singrauli. Emissions from TPPs are among the major sources of black carbon (BC) soot in the atmosphere. Knowledge of BC emissions from TPPs is important in characterizing regional carbonaceous particulate emissions, understanding the fog/haze/smog formation, evaluating regional climate forcing, modeling aerosol optical parameters and concentrations of black carbon, and evaluating human health. Furthermore, elevated BC concentrations, over the Indo-Gangetic Plain (IGP) and the Himalayan foothills, have emerged as an important subject to estimate the effects of deposition and atmospheric warming of BC on the accelerated melting of snow and glaciers in the Himalaya. For the first time, this study reports BC concentrations and aerosol optical parameters near dense coal-fired power plants and open cast coal mining adjacent to the east IGP. In-situ measurements were carried out in Singrauli (located in south-east IGP) at a fixed site about 10 km from power plants and in transit measurements in close proximity to the plants, for few days in the month of January and March 2013. At the fixed site, BC concentration up to the 95 μgm−3 is observed with strong diurnal variations. BC concentration shows two maxima peaks during early morning and evening hours. High BC concentrations are observed in close proximity to the coal-fired TPPs (>200 μgm−3), compared to the outside domain of our study region. Co-located ground-based sunphotometer measurements of aerosol optical depth (AOD) show strong spatial variability at the fixed site, with AOD in the range 0.38–0.58, and the highest AOD in the range 0.7–0.95 near the TPPs in transit measurements (similar to the peak of BC concentrations). Additionally, the Angstrom exponent was found to be in the range 0.4–1.0 (maximum in the morning time) and highest in the proximity of TPPs (~1.0), suggesting abundance of fine particulates, whereas there was low Angstrom exponent over the surrounding coal mining areas. Low Angstrom exponent is characterized by dust from the unpaved roads and nearby coal mining areas. MODIS derived daily AOD shows a good match with the MICROTOPS AOD. The CALIPSO derived subtypes of the aerosol plot shows that the aerosols over Singrauli region are mainly dust, polluted dust, and elevated smoke. The preliminary study for few days provides information about the BC concentrations and aerosol optical properties from Singrauli (one of the NO2 hotspot locations in India). This preliminary study suggests that long-term continuous monitoring of BC is needed to understand the BC concentrations and aerosol optical properties for better quantification and the estimation of the emission to evaluate radiative forcing in the region.

Determinants of traffic related atmospheric particulate matter concentrations and their associated health risk at a highway toll plaza in India