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Emission estimates of Particulate Matter and Heavy Metals from Mobile Sources in Delhi (India)
Abstract
An attempt has been made to make a comprehensive emission inventory of particulate matter (PM) of various size fractions and also of heavy metals (HMs) emitted from mobile sources (both exhaust and non-exhaust) from the road transport of Delhi (1991-2006). COPERT-III and 4 models were mainly used to estimate these emissions. Results show that the annual exhaust emission of PM of size up to 2.5 micrometer (PM2.5) has increased from 3Gg to 4.5Gg during 1991-2006 irrespective of improvement in vehicle-technology and fuel use. PM emission from exhaust and non-exhaust sources in general has increased. Heavy commercial vehicles need attention to control particulate emission as it emerged as a predominant source of PM emissions.
Among non-exhaust emissions of total suspended particulate matter (TSP), road-surface wear (~49%) has the prime contribution. As a result of introduction of unleaded gasoline Pb has significantly reduced (~8 fold) whereas share of Cu and Zn are still considerable.
Among non-exhaust sources, Pb release was the most significant one from tyre-wear whereas from break-wear, Cu release was found to be the most significant followed by Pb and Cr + Zn.
Because of public health concerns further policies need to be developed to reduce emissions of PM and HMs from the road transport of megacity Delhi.
Supplementary resources (2)
... Hassel et al. (1987) identified that approximately 75% of the total lead is emitted into the ambient air. It was hypothesized that 25% of emission of Pb accumulates as Pb or Pb-oxides near the exhaust pipe (Kumari et al., 2013). Lead (Pb) content timeline according to the compliance class is given in Table 4. ...
... The EFs used in the present work were collected from the extensive literature review and expert judgement. Kumari et al. (2013) reported the EFs for estimating the non-exhaust emissions (brake wear and tyre wear) are shown in Table 5. ...
... The silt load may vary, depending on the type of road and its location . The range of the silt loading for different categories of roads Table 5 Emission factor for non-exhaust emissions (brake wear and tyre wear) (Kumari et al., 2013 Table 6 Emission factors for heavy metals based on the fuel type and corresponding range of variation (in μg/kg). were reported by (Gargava et al., 2014;Singh et al., 2020). ...
Clean air is an essential requirement for human health and well-being. The rapid evolution in urbanization poses a risk to human health. The significance of robust emission inventory has intensified with scientific advancement in air quality improvement. A comprehensive GIS-based emission inventory of heavy metals and NMVOCs from road transportation, road dust, and biomass burning covering the megacity of Delhi has been prepared in this study for the base year 2018. The emission of heavy metals from biomass burning and road dust was estimated by computing the chemical constituents of PM 2.5. The emission of heavy metals from all sources is estimated as 695.202 Mg/year, with Zinc (Zn ~ 48%) having the highest emissions, followed by Copper (Cu ~ 21%). The share of road transportation emissions was 78%, followed by resus-pended road dust emissions at 4%. The emissions of 23 NMVOCs (1.158 Gg/year) were calculated from biomass burning (fuelwood, agricultural residue, dung cake, and coal) with Dung cake (~58%) and fuelwood (~41%) as the major contributors. The maximum emission was ethene (~29%), and the minimum was isopropyl benzene (~0.06%). The finding of this study will assist environmental policymakers and stakeholders in developing mitigation methods to reduce emissions and enhance air quality.
... On-road vehicles are responsible for both exhaust and nonexhaust emissions (i.e., brake wear, tire wear, road surface wear, and resuspension of road dust). However, most of the available studies have only focused on exhaust emissions (Baidya and Borken-Kleefeld, 2009;Goel and Guttikunda, 2015;Gurjar et al., 2004;Jain et al., 2016;Mohan et al., 2012;Nesamani, 2010;Sadavarte and Venkataraman, 2014) and in some cases specific non-exhaust source, i.e. particulate matter resuspension, brake wear, tire wear, and road wear (Gargava et al., 2014;Guttikunda and Calori, 2013;Guttikunda et al., 2019a;Kumari et al., 2013;Majumdar et al., 2020;Nagpure et al., 2016;Sahu et al., 2011). Currently, studies on contribution analysis of both exhaust and non-exhaust vehicular emissions at high resolution (ward and village scale) for both urban and rural areas of any region of India are unavailable. ...
... Studies carried out by Sahu et al., 2011, Guttikunda and Calori, 2013, Gargava et al., 2014 estimated emissions for particulate matter resuspension but did not consider any type of wear emissions. Emission estimation study by Nagpure et al., 2016 calculated particulate matter resuspension leaving out emissions from road abrasion, whereas Kumari et al., 2013 only estimated all nonexhaust emissions except particulate matter resuspension. V. Singh et al., 2020 included all types of wear as well as particulate matter resuspension emissions. ...
Air quality deterioration due to vehicular emissions in smaller Indian cities and rural areas remains unacknowledged, even though the situation is alarmingly similar to megacities. The resulting lack of knowledge on travel behavior and vehicle characteristics impacts accuracy of emission studies in these regions. This study uses a novel approach and appropriate primary and secondary data sets to allocate vehicular activities (vehicle population and vehicle kilometer travelled) and associated emissions at a high spatial resolution for estimation and dispersion analysis of vehicular exhaust and non-exhaust PM2.5 emission in an Indian urban-rural landscape. The study indicates that using approaches that do not allocate vehicles kilometers travelled to areas of their expected travel results in underestimating the percent share of PM2.5 emissions from rural roads and motorways while overestimating overall PM2.5 emissions. Particulate matter resuspension is the dominant form of PM2.5 emissions from the vehicular sector on all road types, constituting an even higher fraction on rural roads. Two-wheelers contribute a high fraction of PM2.5 emissions (exhaust and non-exhaust combined), followed by heavy commercial vehicles and four-wheelers on urban roads. Light commercial vehicles, especially agricultural tractors dominate these emissions on rural roads. PM2.5 hotspots are prevalent in urban areas, but several rural areas also experience heavy particulate matter concentrations. Thus, vehicle movement incorporation results in more accurate emission estimation, especially in an urban-rural landscape.
... The rate at which small particles are emitted from a road is determined by this factor. The greatest important contribution from non-exhaust sources is road surface wear: paved or unpaved [29]. According to the US EPA, road dust is commonly referred to "silt loading", which is defined as the mass of sedimentary material with a physical diameter of 75 µm or smaller per unit area of road evaluated. ...
Introduction: In both developed and developing countries, re-suspension of dust particles along the road owing to tire and brake wear is the most common source of Particulate Matters (PM) pollution in metropolitan areas. This study in Douala analyses the effects of paved and unpaved roads on particle matters concentration thresholds in urban environments.
Materials and methods: The United States Environmental Protection Agency (US EPA)'s model AP-42 equations were used to calculate the amount of particle matter emissions on the roads. Between 6 am and 8 pm, a traffic analysis using information from the city of Douala was conducted. The busiest times for traffic were from 8 to 9 a.m. and 6 to 7 p.m. We applied a two-dimensional Gaussian model to determine the particle concentration. Two different scenarios were taken into account: Compared to Scenario 2 (S2), Scenario 1 (S1) represents an unpaved road. The PM10 and PM2.5 types of particles were the main topics of interest.
Results: We obtained for S1, around 917.70 µg/m3 and 559.00 µg/m3 respectively for PM10 and PM2.5. We got roughly 170.00 µg/m3 and 103.90 µg/m3 for S2, respectively for the two particles. The amount of silt deposited
on the road, the kind of road (paved or unpaved), the number, and the types of vehicles moving all influence the emission of road dust re-suspension. Regardless of particle size, these pollution levels are beyond World Health Organization (WHO) recommended norms.
Conclusion: This study offers important information on Douala's pollution levels, which can be a significant cause of disease in the area and should be considered.
... However, the study is limited to a fixed major traffic intersection only. Kumari et al. (2013) used the COPERT-3 emission factor to estimate emission for Indian cities, focusing on the multi-year (1991)(1992)(1993)(1994)(1995)(1996)(1997)(1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006) evolution of vehicular emission. However, this study estimates the total emissions based on registered vehicles and does not provide spatial segregation. ...
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 h 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 PM (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 (oxides of nitrogen), 64 Gg for VOC (volatile organic compound), 0.28 Gg for NH3 (ammonia), 0.26 Gg for N2O (nitrous oxide) and 11.38 Gg for CH4 (methane) for 2018 with an uncertainty of 60 %–68 %. 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 PM, BC and NOx, and within them the 2W (two-wheeler motorcycles) are the major contributors. Diesel-fuelled vehicles contribute most of the PM emission. Diesel and CNG (compressed natural gas) vehicles have a substantial contribution in NOx emission. This study provides very detailed spatiotemporal 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).
... The estimated contribution of exhaust and non-exhaust emissions to total PM 2.5 and PM 10 road traffic emissions are compared with previous studies as shown in Fig. 8 (c, d). Almost all the previous studies (Guttikunda and Calori, 2013;Kumari et al., 2013;Gargava et al., 2014;Nagpure et al., 2016;Singh et al., 2020) in India conducted vehicular emission inventory for the city of Delhi, except the study, Tomar et al., (2022), which carried out at Saharanpur district in Northern India. The estimated contribution of non-exhaust emissions (90%) to PM 10 road traffic emissions in this study is quite higher or similar to previous studies in Delhi (66-90%). ...
Vehicular emissions are the major source of air quality deterioration in Indian megacities. However, there is uncertainty in vehicular emission estimation due to the paucity of vehicular use and travel characteristics, and there is no specific methodology to assess the same. Thus, this study presents a methodology to capture the urban in-use vehicular characteristics. Additionally, it evaluates current vehicular emissions in Mumbai and estimates future emission levels for the year 2030, taking into account various policy interventions. Data for the study were collected via questionnaire surveys at fuel stations across Greater Mumbai – a first in western India. Exhaust and non-exhaust vehicular emissions were developed using the “bottom-up” methodology. Six scenarios were tested for exhaust vehicular emissions and energy consumption under various policy interventions. Monte-Carlo Simulations (MCS) were carried out to find the uncertainties in the vehicular emission estimation. Results showed that approximately 66% of the registered vehicles ply on Mumbai roads, and the on-road fuel efficiency is 12–33% less than the reported lab-based studies. Our study findings suggest that conducting surveys at three fuel stations is adequate for determining urban in-use vehicular characteristics with <5% bias. Reduction in vehicular emissions calls for stringent norms for private passenger vehicles and regulation of non-exhaust vehicular emissions. Given projected vehicular emissions for 2030, urban cities like Mumbai will have to inevitably replace conventional vehicles with electric vehicles to achieve the Paris agreement, which is to limit global warming well below 2 °C.
... Chennai air pollution was least of the four. 3 Air pollution is responsible for lung cell damage, inflammatory responses, impairment of pulmonary host defenses, and acute changes in lung function and respiratory symptoms as well as chronic changes in lung cells and airways. Many Indian cities are not safe for breathing. ...
Air pollution is much big problem on our earth surface prior human evolution, at that time volcanic eruptions, forest fires and meteoroid impacts are certain sources of air pollution. It is historic phenomena as old as human culture.
In the present study was undertaken to collect information from traditional healers on the use of
medicinal plants in peth Taluka of Nashik district Maharashtra. It was carried out during 2016-2017 and this
region mostly situated in western ghats. The area is inhabited by large number of tribes viz. kokna, bhil, Mahadevkolis,
warali, thakur and katkari. Therefore, the main objective in the study to document indigenous knowledge of
plants local traditional healers used for the treatment of cure diarrhea and dysentery. During the investigation, we
observed 38 different plant species belonging 28 families were recorded. They are listed along with their botanical
name, family name, common name and plant parts used with present study.
Trichomes meaning hair are fine outgrowth appendages on plants. They are of diverse structure and
function. Examples are hairs, glandular hairs, scales, and papillae. They also control leaf temperature as well as
water loss. The greatest significance of trichomes is in the identification of plants. Structurally, trichomes may be
unicellular or multicellular, and are classified as glandular or non- glandular. Trichomes arise from epidermal cells
and they are common in all terrestrial plants. Trichomes, as a plant protective barrier against natural hazard such
as herbivores, plants from extreme high or low temperature, drought, ultraviolet (UV) irradiation, pathogen
attacks and excessive transpiration. In this study there are 22 plants belonging to 11 families were studied.
Trichomes are widely found on the aerial parts of a range of plants e.g. stem, leaf region has majority of trichomes
present on it. The present study shows the diversity of trichomes, their morphological differences and which type
of trichome found majority among studied plants. According to plants growing in different habitat to improve the
environmental adaptability and yield of the plans, we can promote or inhabit the trichomes formation by altering
the certain physiological characteristic of trichomes for different plants in the future. So it deducted from this work
more comprehensive research is necessary for their further elaboration.
Air pollution is much big problem on our earth surface prior human evolution, at that time volcanic eruptions, forest fires and meteoroid impacts are certain sources of air pollution. During Lock down due to covod-19 an opportunity to study air quality of Four metro cities of Maharashtra state Mumbai and Pune have worst condition in case of particulate matters.
A study of the winter time size distribution and source apportionment of total suspended particulate matter (TSPM) and associated heavy metal concentrations have been carried out for the city of Delhi. This study is important from the point of view of implementation of compressed natural gas (CNG) as alternate of diesel fuel in the public transport system in 2001 to reduce the pollution level. TSPM were collected using a five-stage cascade impactor at six sites in the winters of 2005–06. The results of size distribution indicate that a major portion (~40%) of TSPM concentration is in the form of PM0.7 (
Quantifying the emissions and concentrations of heavy metals in urban air is a prerequisite for assessing their health effects. In this paper a combination of measurements and modelling is used to assess the contribution from road traffic emissions. Concentrations of particulate heavy metals in air were measured simultaneously during 1 year at a densely trafficked street and at an urban background site in Stockholm, Sweden. Annual mean concentrations of cadmium were 50 times lower than the EU directive and for nickel and arsenic concentrations were 10 and six times lower, respectively. More than a factor of two higher concentrations was in general observed at the street in comparison to roof levels indicating the strong influence from local road traffic emissions. The only compound with a significantly decreasing trend in the urban background was Pb with 9.1 ng m−3 in 1995/96 compared to 3.4 ng m−3 2003/04. This is likely due to decreased emissions from wear of brake linings and reduced emissions due to oil and coal combustion in central Europe.
In a road simulator study, a significant source of sub-micrometer fine particles produced by the road–tire interface was observed. Since the particle size distribution and source strength is dependent on the type of tire used, it is likely that these particles largely originate from the tires, and not the road pavement. The particles consisted most likely of mineral oils from the softening filler and fragments of the carbon-reinforcing filler material (soot agglomerates). This identification was based on transmission electron microscopy studies of collected ultrafine wear particles and on-line thermal treatment using a thermodesorber.The mean particle number diameters were between 15–50 nm, similar to those found in light duty vehicle (LDV) tail-pipe exhaust. A simple box model approach was used to estimate emission factors in the size interval 15–700 nm. The emission factors increased with increasing vehicle speed, and varied between 3.7×1011 and 3.2×1012 particles vehicle−1 km−1 at speeds of 50 and 70 km h−1. This corresponds to between 0.1–1% of tail-pipe emissions in real-world emission studies at similar speeds from a fleet of LDV with 95% gasoline and 5% diesel-fueled cars. The emission factors for particles originating from the road–tire interface were, however, similar in magnitude to particle number emission factors from liquefied petroleum gas-powered vehicles derived in test bench studies in Australia 2005. Thus the road–tire interface may be a significant contributor to particle emissions from ultraclean vehicles.
A comprehensive emission inventory for megacity Delhi, India, for the period 1990-2000 has been developed in support of air quality, atmospheric chemistry and climate studies. It appears that SO 2 and total suspended particles (TSP) are largely emitted by thermal power plants (~68% and ~80%, respectively), while the transport sector contributes most to NO x , CO and non-methane volatile organic compound (NMVOC) emissions (480%). Further, while CO 2 has been largely emitted by power plants in the past (about 60% in 1990, and 48% in 2000), the contribution by the transport sector is increasing (27% in 1990 and 39% in 2000). NH 3 and N 2 O are largely emitted from agriculture (~70% and ~50%, respectively), and solid waste disposal is the main source of CH 4 (~80%). In the past TSP abatement to improve air quality has largely focused on traffic emissions; however, our results suggest that it would be most efficient to also reduce TSP emissions by power plants. We also assessed the potential large-scale transport of the Delhi emissions based on 10-day forward trajectory calculations. The relatively strong growth of NO x emissions indicates that photochemical O 3 formation in the regional environment may be increasing substantially, in particular in the dry season. During the summer, on the other hand, convective mixing of air pollutants may reduce regional but increase large-scale, i.e. hemispheric effects.
Emissions of metals and particulate matter due to wear of brake linings were discussed. The total wear of brake linings was estimated using the total yearly amount of transport for different vehicle types. In order to calculate heavy metals emissions, the metal content of different brake linings were analyzed. Analysis was performed using plasma emission spectrometry.
Mobile sources are significant contributors to ambient particulate matter (PM) in the United States. As the emphasis shifts from PM10 to PM2.5, it becomes particularly important to account for the mobile source contribution to observed particulate levels since these sources may be the major contributor to the fine particle fraction. This is due to the fact that most mobile source mass emissions have an aerodynamic diameter less than 2.5 m, while the particles of geological origin that tend to dominate the PM10 fraction generally have an aerodynamic diameter greater than 2.5 m. A common approach to assess the relative contributions of sources to observed particulate mass concentrations is the application of source apportionment methods. These methods include material balance, chemical mass balance (CMB), and multivariate receptor models. This paper describes a number recent source attribution studies performed in the United States in order to evaluate the range of the mobile source contribution to observed PM. In addition, a review of the methods used to apportion source contributions to ambient particulate loadings is presented.
An emission inventory was compiled for heavy metal air emissions from road transport in Europe (EU-40). For the database, country-specific data was taken such as the diesel and gasoline fuel consumption per country, the content of Pb in gasoline and diesel fuel and the share of different vehicle types. For tyre and brake wear emissions, average wear rates and heavy metal contents of different materials were used to develop emission factors for tyre and brake wear. It covers exhaust emissions (Pb from gasoline and diesel) as well as non-exhaust emissions (As, Cd, Cr, Ni and Pb from the wear of brake linings and vehicle tyres). The base year is 2000, and two scenarios were developed for 2010, a business as usual (BAU) scenario and a maximum feasible technical reduction (MFTR) scenario. Both result in a remarkable decrease in Pb exhaust emissions and a rising share of non-exhaust emissions. To assess the results, the inventory is (a) compared to an inventory compiled with a top-down approach that covers the same area and years but only emissions from combustion processes and (b) added to an inventory covering all sectors for heavy metal air emissions.
Several concepts and indicators exist to measure and rank urban areas in terms of their socio-economic, infrastructural, and environment-related parameters. The World Bank regularly publishes the World Development Indicators (WDI), and the United Nations reports the City Development Index (CDI) and also ranks megacities on the basis of their population size. Here, we evaluate and rank megacities in terms of their trace gas and particle emissions and ambient air quality. Besides ranking the megacities according to their surface area and population density, we evaluate them based on carbon monoxide (CO) emissions per capita, per year, and per unit surface area. Further, we rank the megacities according to ambient atmospheric concentrations of criteria pollutants, notably total suspended particles (TSP), sulfur dioxide (SO2), and nitrogen dioxide (NO2). We propose a multi-pollutant index (MPI) considering the combined level of the three criteria pollutants (i.e., TSP, SO2, and NO2) in view of the World Health Organization (WHO) Guidelines for Air Quality. Of 18 megacities considered here 5 classify as having “fair” air quality, and 13 as “poor”. The megacities with the highest MPI, Dhaka, Beijing, Cairo, and Karachi, most urgently need reduction of air pollution.
Air pollution contributes to mortality and morbidity. We estimated the impact of outdoor (total) and traffic-related air pollution on public health in Austria, France, and Switzerland. Attributable cases of morbidity and mortality were estimated.
Epidemiology-based exposure-response functions for a 10 microg/m3 increase in particulate matter (PM10) were used to quantify the effects of air pollution. Cases attributable to air pollution were estimated for mortality (adults > or = 30 years), respiratory and cardiovascular hospital admissions (all ages), incidence of chronic bronchitis (adults > or = 25 years), bronchitis episodes in children (< 15 years), restricted activity days (adults > or = 20 years), and asthma attacks in adults and children. Population exposure (PM10) was modelled for each km2. The traffic-related fraction was estimated based on PM10 emission inventories.
Air pollution caused 6% of total mortality or more than 40,000 attributable cases per year. About half of all mortality caused by air pollution was attributed to motorised traffic, accounting also for: more than 25,000 new cases of chronic bronchitis (adults); more than 290,000 episodes of bronchitis (children); more than 0.5 million asthma attacks; and more than 16 million person-days of restricted activities.
This assessment estimates the public-health impacts of current patterns of air pollution. Although individual health risks of air pollution are relatively small, the public-health consequences are considerable. Traffic-related air pollution remains a key target for public-health action in Europe. Our results, which have also been used for economic valuation, should guide decisions on the assessment of environmental health-policy options.