Source apportionment of surface-level trace gases and particulate matter at three tropical coastal sites in India

2021 ◽  
Author(s):  
Abhishek Chhari ◽  
Vinay Kumar Dhadwal ◽  
Lokesh Kumar Sahu ◽  
Bomidi Lakshmi Madhavan ◽  
Trupti Das ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
South Asia ◽  
Mass Concentration ◽  
Trace Gases ◽  
Concentration Data ◽  
Gangetic Plain ◽  
Surface Mass ◽  
Temporal And Spatial ◽  
Significant Attention

<p>Over last two decades, South Asia has witnessed a rapid increase in population, industrialization, and energy demands. Consequently, 2-6 fold increase in the emission of particulate matter (PM) and trace gases were reported. Air pollution in South Asia has more adverse impact and is linked to nearly 1 million premature deaths and around 10 million tonnes of crop loss in a year. So, monitoring of trace gases and PM concentrations over urban centers has received significant attention among scientists, policymakers, health regulatory agencies, and the media. Particularly over the Indian region, this becomes significant, as the observation of trace gases and PM concentrations with fairly good temporal and spatial resolutions is limited. Concerns about air quality and transport pathways on a regional scale also place more stringent demand on observations and modeling effort. Quantifying the source contribution (regional emission due to various anthropogenic activities such as city traffic density vs. long-range transport due to meteorological influence) of trace gases and PM over different temporal and spatial scales has been receiving significant attention. In view of this, measurement of trace gases and PM in concurrence with meteorological variables (wind speed and direction) is of paramount importance.</p><p> </p><p>In this study, we have presented three-year surface measurements of TGs (O<sub>3</sub>, CO, NO<sub>x</sub>, SO<sub>2</sub> and NH<sub>3</sub>) and PMs (PM2.5 and PM10) at three coastal and urban sites, namely, Trivandrum (TVM, 8.5°N, 76.9°E, 5m AMSL), Chennai (CHN, 13.7°N, 80.2°E, 6.7m AMSL) and Bhubaneswar (BHB, 20.2°N, 85.8°E, 45m AMSL) located in India. -In addition to that Ozone Monitoring Instrument OMI’s, surface mass concentration data for SO<sub>2</sub> and Moderate Resolution Imaging Spectroradiometer (MODIS) fire counts data were also used to identify potential sources. The principal component analysis (PCA) and concentrated weighted trajectories (CWT) were applied to the dataset. The TGs and PM showed high values during winter and lower values in a monsoon at these sites. Both TGs and PM values were higher at BHB compared to those at TVM and CHN.  Surface O<sub>3</sub> at BHB was about 3 times higher than that at TVM and 2.2 times higher than that at CHN.  Interestingly, PCA suggests that the major concentrations of O<sub>3</sub>, PM10, and SO<sub>2</sub> at TVM and CHN were transported from different locations and not produced locally except for pre-monsoon at CHN, which was of local origin.  CWT analysis and OMI’s surface mass concentration data also suggest that the air quality at TVM could be influenced by heavy emissions transported from the Indo-Gangetic plain. The Merra-2 reanalysis well captured seasonal variations of TGs and PMs; and it overestimated surface O<sub>3</sub>, by a factor of about 2 to the measurement at the study sites.   </p>

scholarly journals Urban influence on the concentration and composition of submicron particulate matter in central Amazonia

2018 ◽  
Author(s):  
Suzane S. de Sá ◽  
Brett B. Palm ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Weiwei Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Air Quality ◽  
Nitrogen Oxides ◽  
Mass Concentration ◽  
Critical Role ◽  
Metropolitan Region ◽  
Anthropogenic Emissions ◽  
Organic Mass ◽  
The City

Abstract. Fundamental to quantifying the influence of human activities on climate and air quality is an understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM). The central Amazon basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the T3 research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented on average 79 ± 7 % of the non-refractory PM1 mass concentration, which was in the same range as several upwind sites. The organic PM1 was, however, considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73 ± 15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27 ± 15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NOy, ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct airmass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared to background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO2-dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of pre-existing atmospheric PM can be accelerated. The combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.

scholarly journals One-Year Simulation of Ozone and Particulate Matter in China Using WRF/CMAQ Modeling System

2016 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research & Forecasting model (WRF) and the Community Multi-scale Air Quality model (CMAQ) was conducted to provide detailed temporal and spatial information of ozone (O3), PM2.5 total and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, over-prediction of O3 generally occurs at low concentration range while under-prediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in Southern China than in Northern, Central and Sichuan basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of CMAQ model in reproducing severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

scholarly journals The Effects of Outdoor Source on Pollution Characteristics and Dynamic Changes of Particulate Matter in an Office

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Guozhong Zheng ◽  
Yuzhen Lu ◽  
Yajing Wang ◽  
Zhengzheng Zhao ◽  
Ke Li ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Indoor Air Quality ◽  
Indoor Air ◽  
Mass Concentration ◽  
Dynamic Changes ◽  
Maximum Increase ◽  
Pollution Characteristics ◽  
Indoor Particulate Matter ◽  
Outdoor Pm10

The indoor air quality has a direct impact on human health. Particulate matter is one of the important factors affecting the indoor air quality. The paper selects an office as the study object and studies the pollution characteristics and dynamic changes of indoor particulate matter in different outdoor pollution levels. The mass concentration of outdoor PM10 is used as the evaluation basis of the outdoor pollution level. The outdoor PM10 concentration levels are divided into the range of 200–300, 300–400, 400–500, 500–600, 600–700 μg·m−3, individually. Firstly, the change characteristics of the mass concentration and the number concentration of the particulate matter in the five outdoor conditions are analyzed. Secondly, the maximum increase values and the maximum increase rates of the mass concentrations of different particle sizes in the five conditions are compared. Then, the penetration factors of the particulates in different sizes are compared among the five conditions. Finally, the correlation between indoor particulate matter and outdoor particulate matter is studied. The study results show that the effect of outdoor infiltration has a great influence on the indoor PM1 mass concentration, and the penetrating factors of the particulate matter between 0.3 μm and 0.5 μm are higher than 0.6; their permeability is the most obvious.

scholarly journals Urban influence on the concentration and composition of submicron particulate matter in central Amazonia

2018 ◽  
Vol 18 (16) ◽  
pp. 12185-12206 ◽  
Author(s):  
Suzane S. de Sá ◽  
Brett B. Palm ◽  
Pedro Campuzano-Jost ◽  
Douglas A. Day ◽  
Weiwei Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Particulate Matter ◽  
Air Quality ◽  
Nitrogen Oxides ◽  
Mass Concentration ◽  
Critical Role ◽  
Metropolitan Region ◽  
Anthropogenic Emissions ◽  
Organic Mass ◽  
The City

Abstract. An understanding of how anthropogenic emissions affect the concentrations and composition of airborne particulate matter (PM) is fundamental to quantifying the influence of human activities on climate and air quality. The central Amazon Basin, especially around the city of Manaus, Brazil, has experienced rapid changes in the past decades due to ongoing urbanization. Herein, changes in the concentration and composition of submicron PM due to pollution downwind of the Manaus metropolitan region are reported as part of the GoAmazon2014/5 experiment. A high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) and a suite of other gas- and particle-phase instruments were deployed at the “T3” research site, 70 km downwind of Manaus, during the wet season. At this site, organic components represented 79±7 % of the non-refractory PM1 mass concentration on average, which was in the same range as several upwind sites. However, the organic PM1 was considerably more oxidized at T3 compared to upwind measurements. Positive-matrix factorization (PMF) was applied to the time series of organic mass spectra collected at the T3 site, yielding three factors representing secondary processes (73±15 % of total organic mass concentration) and three factors representing primary anthropogenic emissions (27±15 %). Fuzzy c-means clustering (FCM) was applied to the afternoon time series of concentrations of NOy, ozone, total particle number, black carbon, and sulfate. Four clusters were identified and characterized by distinct air mass origins and particle compositions. Two clusters, Bkgd-1 and Bkgd-2, were associated with background conditions. Bkgd-1 appeared to represent near-field atmospheric PM production and oxidation of a day or less. Bkgd-2 appeared to represent material transported and oxidized for two or more days, often with out-of-basin contributions. Two other clusters, Pol-1 and Pol-2, represented the Manaus influence, one apparently associated with the northern region of Manaus and the other with the southern region of the city. A composite of the PMF and FCM analyses provided insights into the anthropogenic effects on PM concentration and composition. The increase in mass concentration of submicron PM ranged from 25 % to 200 % under polluted compared with background conditions, including contributions from both primary and secondary PM. Furthermore, a comparison of PMF factor loadings for different clusters suggested a shift in the pathways of PM production under polluted conditions. Nitrogen oxides may have played a critical role in these shifts. Increased concentrations of nitrogen oxides can shift pathways of PM production from HO2-dominant to NO-dominant as well as increase the concentrations of oxidants in the atmosphere. Consequently, the oxidation of biogenic and anthropogenic precursor gases as well as the oxidative processing of preexisting atmospheric PM can be accelerated. This combined set of results demonstrates the susceptibility of atmospheric chemistry, air quality, and associated climate forcing to anthropogenic perturbations over tropical forests.

scholarly journals One-year simulation of ozone and particulate matter in China using WRF/CMAQ modeling system

2016 ◽  
Vol 16 (16) ◽  
pp. 10333-10350 ◽  
Author(s):  
Jianlin Hu ◽  
Jianjun Chen ◽  
Qi Ying ◽  
Hongliang Zhang
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Model Performance ◽  
Monitoring Network ◽  
Organic Aerosol ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Low Concentration ◽  
Temporal And Spatial

Abstract. China has been experiencing severe air pollution in recent decades. Although an ambient air quality monitoring network for criteria pollutants has been constructed in over 100 cities since 2013 in China, the temporal and spatial characteristics of some important pollutants, such as particulate matter (PM) components, remain unknown, limiting further studies investigating potential air pollution control strategies to improve air quality and associating human health outcomes with air pollution exposure. In this study, a yearlong (2013) air quality simulation using the Weather Research and Forecasting (WRF) model and the Community Multi-scale Air Quality (CMAQ) model was conducted to provide detailed temporal and spatial information of ozone (O3), total PM2.5, and chemical components. Multi-resolution Emission Inventory for China (MEIC) was used for anthropogenic emissions and observation data obtained from the national air quality monitoring network were collected to validate model performance. The model successfully reproduces the O3 and PM2.5 concentrations at most cities for most months, with model performance statistics meeting the performance criteria. However, overprediction of O3 generally occurs at low concentration range while underprediction of PM2.5 happens at low concentration range in summer. Spatially, the model has better performance in southern China than in northern China, central China, and Sichuan Basin. Strong seasonal variations of PM2.5 exist and wind speed and direction play important roles in high PM2.5 events. Secondary components have more boarder distribution than primary components. Sulfate (SO42−), nitrate (NO3−), ammonium (NH4+), and primary organic aerosol (POA) are the most important PM2.5 components. All components have the highest concentrations in winter except secondary organic aerosol (SOA). This study proves the ability of the CMAQ model to reproduce severe air pollution in China, identifies the directions where improvements are needed, and provides information for human exposure to multiple pollutants for assessing health effects.

scholarly journals Airborne particulate matter monitoring in Kenya using calibrated low cost sensors

2018 ◽  
Author(s):  
Francis D. Pope ◽  
Michael Gatari ◽  
David Ng’ang’a ◽  
Alexander Poynter ◽  
Rhiannon Blake
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Mass Concentration ◽  
Low Cost ◽  
Urban Background ◽  
Rural Background ◽  
The Mean ◽  
Urban Sites ◽  
Urban Background Site

Abstract. East African countries face an increasing threat from poor air quality, stemming from rapid urbanisation, population growth and a steep rise in fuel use and motorization rates. With few air quality monitoring systems available, this study provides the much needed high temporal resolution data to investigate the concentrations of particulate matter (PM) air pollution in Kenya. Calibrated low cost optical particle counters (OPCs) were deployed in Kenya in three locations: two in the capital of Nairobi and one in a rural location in the outskirts of Nanyuki, which is upwind of Nairobi. The two Nairobi sites consist of an urban background site and a roadside site. The instruments were composed of an Alphasense OPC-N2 optical particle counter (OPC) ran with a raspberry pi low cost microcomputer, packaged in a weather proof box. Measurements were conducted over a two-month period (February–March 2017) with an intensive study period when all measurements were active at all sites lasting two weeks. When collocated, the three OPC-N2 instruments demonstrated good inter-instrument precision with a coefficient of variance of 8.8 ± 2.0 % in the PM2.5 fraction. The low cost sensors had an absolute PM mass concentration calibration using a collocated gravimetric measurement at the urban background site in Nairobi. The mean daily PM1 mass concentration measured at the urban roadside, urban background and rural background sites were 23.9, 16.1, 8.8 µg m−3. The mean daily PM2.5 mass concentration measured at the urban roadside, urban background and rural background sites were 36.6, 24.8, 13.0 µg m−3. The mean daily PM10 mass concentration measured at the urban roadside, urban background and rural background sites were 93.7, 53.0, 19.5 µg m−3. The urban measurements in Nairobi showed that particulate matter concentrations regularly exceed WHO guidelines in both the PM10 and PM2.5 size ranges. Following a Lenschow type approach we can estimate the urban and roadside increments that are applicable to Nairobi. Median urban and roadside increments are 33.1 and 43.3 µg m−3 for PM10, respectively, the median urban and roadside increments are 7.1 and 18.3 µg m−3 for PM2.5, respectively, and the median urban and roadside increments are 4.7 and 12.6 µg m−3 for PM1, respectively. These increments highlight the importance of both the urban and roadside increments to urban air pollution in Nairobi. A clear diurnal behaviour in PM mass concentration was observed at both urban sites, which peaks during the morning and evening Nairobi rush hours; this was consistent with the high measured roadside increment indicating vehicular traffic being a dominant source of particulate matter in the city, accounting for approximately 48.1, 47.5, and 57.2 % of the total particulate matter loading in the PM10, PM2.5 and PM1 size ranges, respectively. Collocated meteorological measurements at the urban sites were collected, allowing for an understanding of the location of major sources of particulate matter at the two sites. The potential problems of using low cost sensors for PM measurement without gravimetric calibration available at all sites are discussed. This study shows that calibrated low cost sensors can be used successfully to measure air pollution in cities like Nairobi. It demonstrates that low cost sensors could be used to create an affordable and reliable network to monitor air quality in cities.

scholarly journals Pilot Study of Air Quality during Pre and Post COVID-19 Lockdown: An Inadvertent Assistance to the Environment

2021 ◽  
pp. 213-220
Author(s):  
Akhtar Shareef ◽  
Durdana Rais Hashmi
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Pilot Study ◽  
Urban Areas ◽  
Air Pollutants ◽  
Virus Disease ◽  
Trace Gases ◽  
Industrial Areas ◽  
Criteria Pollutants ◽  
Novel Coronavirus

The main object of this study was to examine the levels of air quality in Karachi, Pakistan, before and during the 1st, 2nd and 3rd wave of lockdown period levied to control the spread of a novel coronavirus (COVID-19) in the environment of Karachi city. Momentous improvement in the air quality has been found during the ‘Lockdown’ being implemented due to the Corona Virus Disease (COVID -19) pandemic in Karachi city. Concentrations of trace gases and particulate matter were used to calculate the results according to the criteria of USEPA. We have analyzed data from fourteen different locations along the busy roads in commercial, residential and industrial areas of Karachi during the period of lockdown. Data were compared to the before lockdown (BL) and during the complete lockdown (CL 1stwave), smart lockdown (SL 2nd wave) and again complete lockdown (CL-2 3rd wave) of COVID pandemic. The results show drastic reductions in criteria pollutants (PM10, CO, SO2 and NOx) concentrations in all the selected area during lockdown period. This study explained the level of air quality and its relation to prepare alternative plans to mitigate the air pollutants and to improve the environment of urban areas.

scholarly journals Spatio-Temporal Variation Characteristics of PM2.5 in the Beijing–Tianjin–Hebei Region, China, from 2013 to 2018

2019 ◽  
Vol 16 (21) ◽  
pp. 4276 ◽  
Author(s):  
Lili Wang ◽  
Qiulin Xiong ◽  
Gaofeng Wu ◽  
Atul Gautam ◽  
Jianfang Jiang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Temporal Variation ◽  
Mass Concentration ◽  
Pollution Prevention ◽  
Concentration Data ◽  
Control Plan ◽  
Variation Characteristics ◽  
Spatio Temporal ◽  
Bth Region

Air pollution, including particulate matter (PM2.5) pollution, is extremely harmful to the environment as well as human health. The Beijing–Tianjin–Hebei (BTH) Region has experienced heavy PM2.5 pollution within China. In this study, a six-year time series (January 2013–December 2018) of PM2.5 mass concentration data from 102 air quality monitoring stations were studied to understand the spatio-temporal variation characteristics of the BTH region. The average annual PM2.5 mass concentration in the BTH region decreased from 98.9 μg/m3 in 2013 to 64.9 μg/m3 in 2017. Therefore, China has achieved its Air Pollution Prevention and Control Plan goal of reducing the concentration of fine particulate matter in the BTH region by 25% by 2017. The PM2.5 pollution in BTH plain areas showed a more significant change than mountains areas, with the highest PM2.5 mass concentration in winter and the lowest in summer. The results of spatial autocorrelation and cluster analyses showed that the PM2.5 mass concentration in the BTH region from 2013–2018 showed a significant spatial agglomeration, and that spatial distribution characteristics were high in the south and low in the north. Changes in PM2.5 mass concentration in the BTH region were affected by both socio-economic factors and meteorological factors. Our results can provide a point of reference for making PM2.5 pollution control decisions.

Sign in / Sign up

Export Citation Format

Share Document