scholarly journals Impacts of East Mediterranean megacity emissions on air quality

2012 ◽  
Vol 12 (14) ◽  
pp. 6335-6355 ◽  
Author(s):  
U. Im ◽  
M. Kanakidou
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Urban Areas ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Biogenic Emissions ◽  
Anthropogenic Emissions ◽  
East Mediterranean ◽  
Fine Particulate ◽  
Surface O3

Abstract. Megacities are large urban agglomerations with intensive anthropogenic emissions that have significant impacts on local and regional air quality. In the present mesoscale modeling study, the impacts of anthropogenic emissions from the Greater Istanbul Area (GIA) and the Greater Athens Area (GAA) on the air quality in GIA, GAA and the entire East Mediterranean are quantified for typical wintertime (December 2008) and summertime (July 2008) conditions. They are compared to those of the regional anthropogenic and biogenic emissions that are also calculated. Finally, the efficiency of potential country-based emissions mitigation in improving air quality is investigated. The results show that relative contributions from both cities to surface ozone (O3) and aerosol levels in the cities' extended areas are generally higher in winter than in summer. Anthropogenic emissions from GIA depress surface O3 in the GIA by ~ 60% in winter and ~ 20% in summer while those from GAA reduce the surface O3 in the GAA by 30% in winter and by 8% in summer. GIA and GAA anthropogenic emissions contribute to the fine particulate matter (PM2.5) levels inside the cities themselves by up to 75% in winter and by 50% (GIA) and ~ 40% (GAA), in summer. GIA anthropogenic emissions have larger impacts on the domain-mean surface O3 (up to 1%) and PM2.5 (4%) levels compared to GAA anthropogenic emissions (<1% for O3 and ≤2% for PM2.5) in both seasons. Impacts of regional anthropogenic emissions on the domain-mean surface pollutant levels (up to 17% for summertime O3 and 52% for wintertime fine particulate matter, PM2.5) are much higher than those from Istanbul and Athens together (~ 1% for O3 and ~ 6% for PM2.5, respectively). Regional biogenic emissions are found to limit the production of secondary inorganic aerosol species in summer up to 13% (non-sea-salt sulfate (nss-SO42−) in rural Athens) due to their impact on oxidant levels while they have negligible impact in winter. Finally, the responses to country-based anthropogenic emission mitigation scenarios inside the studied region show increases in O3 mixing ratios in the urban areas of GIA and GAA, higher in winter (~ 13% for GIA and 2% for GAA) than in summer (~ 7% for GIA and <1% for GAA). On the opposite PM2.5 concentrations decrease by up to 30% in GIA and by 20% in GAA with the highest improvements computed for winter. The emission reduction strategy also leads to domain-wide decreases in most primary pollutants like carbon monoxide (CO) or nitrogen oxides (NOx) for both seasons. The results show the importance of long range transport of pollutants for the air quality in the East Mediterranean. Thus, improvements of air quality in the East Mediterranean require coordinated efforts inside the region and beyond.

scholarly journals Sensitivity analysis of the surface ozone and fine particulate matter to meteorological parameters in China

2020 ◽  
Vol 20 (21) ◽  
pp. 13455-13466
Author(s):  
Zhihao Shi ◽  
Lin Huang ◽  
Jingyi Li ◽  
Qi Ying ◽  
Hongliang Zhang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Great Influence ◽  
Meteorological Conditions ◽  
Daily Maximum ◽  
Fine Particulate ◽  
Surface O3

Abstract. Meteorological conditions play important roles in the formation of ozone (O3) and fine particulate matter (PM2.5). China has been suffering from serious regional air pollution problems, characterized by high concentrations of surface O3 and PM2.5. In this study, the Community Multiscale Air Quality (CMAQ) model was used to quantify the sensitivity of surface O3 and PM2.5 to key meteorological parameters in different regions of China. Six meteorological parameters were perturbed to create different meteorological conditions, including temperature (T), wind speed (WS), absolute humidity (AH), planetary boundary layer height (PBLH), cloud liquid water content (CLW) and precipitation (PCP). Air quality simulations under the perturbed meteorological conditions were conducted in China in January and July of 2013. The changes in O3 and PM2.5 concentrations due to individual meteorological parameters were then quantified. T has a great influence on the daily maximum 8 h average O3 (O3-8 h) concentrations, which leads to O3-8 h increases by 1.7 in January in Chongqing and 1.1 ppb K−1 in July in Beijing. WS, AH, and PBLH have a smaller but notable influence on O3-8 h with maximum change rates of 0.3 ppb %−1, −0.15 ppb %−1, and 0.14 ppb %−1, respectively. T, WS, AH, and PBLH have important effects on PM2.5 formation of both in January and July. In general, PM2.5 sensitivities are negative to T, WS, and PBLH and positive to AH in most regions of China. The sensitivities in January are much larger than in July. PM2.5 sensitivity to T, WS, PBLH, and AH in January can be up to −5 µg m−3 K−1, −3 µg m−3 %−1, −1 µg m−3 %−1, and +0.6 µg m−3 %−1, respectively, and in July it can be up to −2 µg m−3 K−1, −0.4 µg m−3 %−1, −0.14 µg m−3 %−1, and +0.3 µg m−3 %−1, respectively. Other meteorological factors (CLW and PCP) have negligible effects on O3-8 h (less than 0.01 ppb %−1) and PM2.5 (less than 0.01 µg m−3 %−1). The results suggest that surface O3 and PM2.5 concentrations can change significantly due to changes in meteorological parameters, and it is necessary to consider these effects when developing emission control strategies in different regions of China.

scholarly journals Sensitivity Analysis of the Surface Ozone and Fine Particulate Matter to Meteorological Parameters in China

2020 ◽  
Author(s):  
Zhihao Shi ◽  
Lin Huang ◽  
Jingyi Li ◽  
Qi Ying ◽  
Hongliang Zhang ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Meteorological Parameters ◽  
Surface Ozone ◽  
Fine Particulate Matter ◽  
Absolute Humidity ◽  
Meteorological Conditions ◽  
Daily Maximum ◽  
Fine Particulate ◽  
Surface O3

Abstract. Meteorological conditions play important roles in the formation of ozone (O3) and fine particulate matter (PM2.5). China has been suffering from serious regional air pollution problems, characterized by high concentrations of surface O3 and PM2.5. In this study, the Community Multiscale Air Quality (CMAQ) model was used to quantify the sensitivity of surface O3 and PM2.5 to key meteorological parameters in different regions of China. Six meteorological parameters were perturbed to create different meteorological conditions, including temperature (T), wind speed (WS), absolute humidity (AH), planetary boundary layer height (PBLH), cloud liquid water content (CLW) and precipitation (PCP). Air quality simulations under the perturbed meteorological conditions were conducted in China in January and July of 2013. The changes in O3 and PM2.5 concentrations due to individual meteorological parameters were then quantified. T has the greatest impact on the daily maximum 8-h average O3 (O3-8 h) concentrations, which leads to O3-8 h increases by 1.7 ppb K−1 in January in Chongqing and 1.1 ppb K−1 in July in Beijing. WS, AH, and PBLH have a smaller but notable influence on O3-8 h with maximum change rates of 0.3, −0.15, and 0.14 ppb %−1, respectively. T, WS, AH, and PBLH have important effects on PM2.5 formation of in both January and July. In general, PM2.5 sensitivities are negative to T, WS, and PBLH and positive to AH in most regions of China. The sensitivities in January are much larger than in July. PM2.5 sensitivity to T, WS, PBLH, and AH in January can be up to −5 μg m−3 K−1, −3 μg m3 %−1, −1 g m−3, and +0.6 μg m−3 %−1, respectively, and in July can be up to −2 μg m−3 K−1, −0.4 μg m−3 %−1, −0.14 μg m−3 %−1, and +0.3 μg m−3 %−1, respectively. Other meteorological factors (CLW and PCP) have negligible effects on O3-8 h (less than 0.01 ppb %−1) and PM2.5 (less than 0.01 μg m−3 %−1). The results suggest that surface O3 and PM2.5 concentrations can change significantly due to changes in meteorological parameters and it is necessary to consider these effects when developing emission control strategies in different regions of China.

scholarly journals Impacts of historical climate and land cover changes on fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia over 1980–2010

2016 ◽  
Author(s):  
Yu Fu ◽  
Amos P. K. Tai ◽  
Hong Liao
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Anthropogenic Emissions ◽  
Voc Emissions ◽  
Fine Particulate

Abstract. To examine the effects of changes in climate, land cover and land use (LCLU), and anthropogenic emissions on fine particulate matter (PM2.5) between the 5-year periods 1981–1985 and 2007–2011 in East Asia, we perform a series of simulations using a global chemical transport model (GEOS-Chem) driven by assimilated meteorological data and a suite of land cover and land use data. Our results indicate that climate change alone could lead to a decrease in wintertime PM2.5 concentration by 4.0–12.0 μg m−3 in northern China, but an increase in summertime PM2.5 by 6.0–8.0 μg m−3 in those regions. These changes are attributable to the changing chemistry and transport of all PM2.5 components driven by long-term trends in temperature, wind speed and mixing depth. The concentration of secondary organic aerosol (SOA) is simulated to increase by 0.2–0.8 μg m−3 in both summer and winter in most regions of East Asia due to climate change alone, mostly reflecting higher biogenic volatile organic compound (VOC) emissions under warming. The impacts of LCLU change alone on PM2.5 (−2.1 to +1.3 μg m−3) are smaller than that of climate change, but among the various components the sensitivity of SOA and thus organic carbon to LCLU change (−0.4 to +1.2 μg m−3) is quite significant especially in summer, which is driven mostly by changes in biogenic VOC emissions following cropland expansion and changing vegetation density. The combined impacts show that while the effect of climate change on PM2.5 air quality is more pronounced, LCLU change could offset part of the climate effect in some regions but exacerbate it in others. As a result of both climate and LCLU changes combined, PM2.5 levels are estimated to change by −12.0 to +12.0 μg m−3 across East Asia between the two periods. Changes in anthropogenic emissions remain the largest contributor to deteriorating PM2.5 air quality in East Asia during the study period, but climate and LCLU changes could lead to a substantial modification of PM2.5 levels.

scholarly journals Impacts of historical climate and land cover changes on fine particulate matter (PM<sub>2.5</sub>) air quality in East Asia between 1980 and 2010

2016 ◽  
Vol 16 (16) ◽  
pp. 10369-10383 ◽  
Author(s):  
Yu Fu ◽  
Amos P. K. Tai ◽  
Hong Liao
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Particulate Matter ◽  
Air Quality ◽  
Land Cover ◽  
East Asia ◽  
Fine Particulate Matter ◽  
Anthropogenic Emissions ◽  
Voc Emissions ◽  
Fine Particulate

Abstract. To examine the effects of changes in climate, land cover and land use (LCLU), and anthropogenic emissions on fine particulate matter (PM2.5) between the 5-year periods 1981–1985 and 2007–2011 in East Asia, we perform a series of simulations using a global chemical transport model (GEOS-Chem) driven by assimilated meteorological data and a suite of land cover and land use data. Our results indicate that climate change alone could lead to a decrease in wintertime PM2.5 concentration by 4.0–12.0 µg m−3 in northern China, but to an increase in summertime PM2.5 by 6.0–8.0 µg m−3 in those regions. These changes are attributable to the changing chemistry and transport of all PM2.5 components driven by long-term trends in temperature, wind speed and mixing depth. The concentration of secondary organic aerosol (SOA) is simulated to increase by 0.2–0.8 µg m−3 in both summer and winter in most regions of East Asia due to climate change alone, mostly reflecting higher biogenic volatile organic compound (VOC) emissions under warming. The impacts of LCLU change alone on PM2.5 (−2.1 to +1.3 µg m−3) are smaller than that of climate change, but among the various components the sensitivity of SOA and thus organic carbon to LCLU change (−0.4 to +1.2 µg m−3) is quite significant especially in summer, which is driven mostly by changes in biogenic VOC emissions following cropland expansion and changing vegetation density. The combined impacts show that while the effect of climate change on PM2.5 air quality is more pronounced, LCLU change could offset part of the climate effect in some regions but exacerbate it in others. As a result of both climate and LCLU changes combined, PM2.5 levels are estimated to change by −12.0 to +12.0 µg m−3 across East Asia between the two periods. Changes in anthropogenic emissions remain the largest contributor to deteriorating PM2.5 air quality in East Asia during the study period, but climate and LCLU changes could lead to a substantial modification of PM2.5 levels.

Hybrid Model for Prediction of Carbon Monoxide and Fine Particulate Matter Concentrations near a Road Intersection

2015 ◽  
Vol 2503 (1) ◽  
pp. 29-38 ◽  
Author(s):  
Zhanyong Wang ◽  
Hong-Di He ◽  
Feng Lu ◽  
Qing-Chang Lu ◽  
Zhong-Ren Peng
Keyword(s):  
Neural Network ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Air Quality ◽  
Hybrid Model ◽  
Urban Areas ◽  
Fine Particulate Matter ◽  
Principal Component ◽  
Fine Particulate ◽  
Proposed Model

Air quality time series near road intersections consist of complex linear and nonlinear patterns and are difficult to forecast. The backpropagation neural network (BPNN) has been applied for air quality forecasting in urban areas, but it has limited accuracy because of the inability to predict extreme events. This study proposed a novel hybrid model called GAWNN that combines a genetic algorithm and a wavelet neural network to improve forecast accuracy. The proposed model was examined through predicting the carbon monoxide (CO) and fine particulate matter (PM2.5) concentrations near a road intersection. Before the predictions, principal component analysis was adopted to generate principal components as input variables to reduce data complexity and collinearity. Then the GAWNN model and the BPNN model were implemented. The comparative results indicated that GAWNN provided more reliable and accurate predictions of CO and PM2.5 concentrations. The results also showed that GAWNN performed better than BPNN did in the capability of forecasting extreme concentrations. Furthermore, the spatial transferability of the GAWNN model was reasonably good despite a degenerated performance caused by the unavoidable difference between the training and test sites. These findings demonstrate the potential of the application of the proposed model to forecast the fine-scale trend of air pollution in the vicinity of a road intersection.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

scholarly journals Meteorological controls on atmospheric particulate pollution during hazard reduction burns

2017 ◽  
Author(s):  
Giovanni Di Virgilio ◽  
Melissa Anne Hart ◽  
Ningbo Jiang
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Fine Particulate Matter ◽  
Meteorological Conditions ◽  
Atmospheric Conditions ◽  
Hazard Reduction ◽  
Particulate Pollution ◽  
Fine Particulate ◽  
Projected Changes ◽  
Sydney Australia

Abstract. Internationally, severe wildfires are an escalating problem likely to worsen given projected changes to climate. Hazard reduction burns (HRB) are used to suppress wildfire occurrences, but they generate considerable emissions of atmospheric fine particulate matter, which depending upon prevailing atmospheric conditions, can degrade air quality. Our objectives are to improve understanding of the relationships between meteorological conditions and air quality during HRBs in Sydney, Australia. We identify the primary meteorological covariates linked to high PM2.5 pollution (particulates

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