scholarly journals Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

2017 ◽  
Author(s):  
Matthieu Pommier ◽  
Hilde Fagerli ◽  
Michael Gauss ◽  
David Simpson ◽  
Sumit Sharma ◽  
...  
Keyword(s):  
Climate Change ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Temporal Correlation ◽  
Public Health Issue ◽  
Anthropogenic Emissions ◽  
Reference Scenario ◽  
Chemical Transport Model ◽  
Data Set

Abstract. Eleven of the world’s 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no more policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to calculate changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r=0.9) and high spatial correlations for PM2.5 (r=0.5 and r=0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6%), the model overestimates O3 by 35%. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given inevitable uncertainties in much of the input data For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the North characterized by a statistically significant increase by up to 4% (2 ppb) and one in the South by a decrease up to -3% (-1.4 ppb). This variation in O3 is found to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic NMVOCs. Our calculations suggest that PM2.5 will increase by up to 6.5% in the 2050s, driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIA), which are mainly affected by the change in precipitation, biogenic emissions and wind speed. The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13% and 67% in average in 2050s, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in 2050s, exceeding OM and dust.

scholarly journals Impact of regional climate change and future emission scenarios on surface O<sub>3</sub> and PM<sub>2.5</sub> over India

2018 ◽  
Vol 18 (1) ◽  
pp. 103-127 ◽  
Author(s):  
Matthieu Pommier ◽  
Hilde Fagerli ◽  
Michael Gauss ◽  
David Simpson ◽  
Sumit Sharma ◽  
...  
Keyword(s):  
Climate Change ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Public Health Issue ◽  
Anthropogenic Emissions ◽  
Reference Scenario ◽  
Chemical Transport Model ◽  
Data Set ◽  
Gangetic Plain

Abstract. Eleven of the world's 20 most polluted cities are located in India and poor air quality is already a major public health issue. However, anthropogenic emissions are predicted to increase substantially in the short-term (2030) and medium-term (2050) futures in India, especially if no further policy efforts are made. In this study, the EMEP/MSC-W chemical transport model has been used to predict changes in surface ozone (O3) and fine particulate matter (PM2.5) for India in a world of changing emissions and climate. The reference scenario (for present-day) is evaluated against surface-based measurements, mainly at urban stations. The evaluation has also been extended to other data sets which are publicly available on the web but without quality assurance. The evaluation shows high temporal correlation for O3 (r =  0.9) and high spatial correlation for PM2.5 (r =  0.5 and r =  0.8 depending on the data set) between the model results and observations. While the overall bias in PM2.5 is small (lower than 6 %), the model overestimates O3 by 35 %. The underestimation in NOx titration is probably the main reason for the O3 overestimation in the model. However, the level of agreement can be considered satisfactory in this case of a regional model being evaluated against mainly urban measurements, and given the inevitable uncertainties in much of the input data.For the 2050s, the model predicts that climate change will have distinct effects in India in terms of O3 pollution, with a region in the north characterized by a statistically significant increase by up to 4 % (2 ppb) and one in the south by a decrease up to −3 % (−1.4 ppb). This variation in O3 is assumed to be partly related to changes in O3 deposition velocity caused by changes in soil moisture and, over a few areas, partly also by changes in biogenic non-methane volatile organic compounds.Our calculations suggest that PM2.5 will increase by up to 6.5 % over the Indo-Gangetic Plain by the 2050s. The increase over India is driven by increases in dust, particulate organic matter (OM) and secondary inorganic aerosols (SIAs), which are mainly affected by the change in precipitation, biogenic emissions and wind speed.The large increase in anthropogenic emissions has a larger impact than climate change, causing O3 and PM2.5 levels to increase by 13 and 67 % on average in the 2050s over the main part of India, respectively. By the 2030s, secondary inorganic aerosol is predicted to become the second largest contributor to PM2.5 in India, and the largest in the 2050s, exceeding OM and dust.

scholarly journals Implementation of Yale Interactive terrestrial Biosphere model v1.0 into GEOS-Chem v12.0.0: a tool for biosphere–chemistry interactions

2020 ◽  
Vol 13 (3) ◽  
pp. 1137-1153 ◽  
Author(s):  
Yadong Lei ◽  
Xu Yue ◽  
Hong Liao ◽  
Cheng Gong ◽  
Lin Zhang
Keyword(s):  
Stomatal Conductance ◽  
Atmospheric Chemistry ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Terrestrial Biosphere ◽  
Area Index

Abstract. The terrestrial biosphere and atmospheric chemistry interact through multiple feedbacks, but the models of vegetation and chemistry are developed separately. In this study, the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic vegetation model with biogeochemical processes, is implemented into the Chemical Transport Model GEOS-Chem (GC) version 12.0.0. Within this GC-YIBs framework, leaf area index (LAI) and canopy stomatal conductance dynamically predicted by YIBs are used for dry deposition calculation in GEOS-Chem. In turn, the simulated surface ozone (O3) by GEOS-Chem affect plant photosynthesis and biophysics in YIBs. The updated stomatal conductance and LAI improve the simulated O3 dry deposition velocity and its temporal variability for major tree species. For daytime dry deposition velocities, the model-to-observation correlation increases from 0.69 to 0.76, while the normalized mean error (NME) decreases from 30.5 % to 26.9 % using the GC-YIBs model. For the diurnal cycle, the NMEs decrease by 9.1 % for Amazon forests, 6.8 % for coniferous forests, and 7.9 % for deciduous forests using the GC-YIBs model. Furthermore, we quantify the damaging effects of O3 on vegetation and find a global reduction of annual gross primary productivity by 1.5 %–3.6 %, with regional extremes of 10.9 %–14.1 % in the eastern USA and eastern China. The online GC-YIBs model provides a useful tool for discerning the complex feedbacks between atmospheric chemistry and the terrestrial biosphere under global change.

scholarly journals Seasonal and spatial variability of surface ozone over China: contributions from background and domestic pollution

2011 ◽  
Vol 11 (7) ◽  
pp. 3511-3525 ◽  
Author(s):  
Y. Wang ◽  
Y. Zhang ◽  
J. Hao ◽  
M. Luo
Keyword(s):  
Yangtze River ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Long Range Transport ◽  
Anthropogenic Emissions ◽  
Spatial Gradient ◽  
Chemical Transport Model ◽  
Background Ozone ◽  
Range Transport

Abstract. Both observations and a 3-D chemical transport model suggest that surface ozone over populated eastern China features a summertime trough and that the month when surface ozone peaks differs by latitude and region. Source-receptor analysis is used to quantify the contributions of background ozone and Chinese anthropogenic emissions on this variability. Annual mean background ozone over China shows a spatial gradient from 55 ppbv in the northwest to 20 ppbv in the southeast, corresponding with changes in topography and ozone lifetime. Pollution background ozone (annual mean of 12.6 ppbv) shows a minimum in the summer and maximum in the spring. On the monthly-mean basis, Chinese pollution ozone (CPO) has a peak of 20–25 ppbv in June north of the Yangtze River and in October south of it, which explains the peaks of surface ozone in these months. The summertime trough in surface ozone over eastern China can be explained by the decrease of background ozone from spring to summer (by −15 ppbv regionally averaged over eastern China). Tagged simulations suggest that long-range transport of ozone from northern mid-latitude continents (including Europe and North America) reaches a minimum in the summer, whereas ozone from Southeast Asia exhibits a maximum in the summer over eastern China. This contrast in seasonality provides clear evidence that the seasonal switch in monsoonal wind patterns plays a significant role in determining the seasonality of background ozone over China.

scholarly journals Seasonal and spatial variability of surface ozone over China: contributions from background and domestic pollution

2010 ◽  
Vol 10 (11) ◽  
pp. 27853-27891 ◽  
Author(s):  
Y. Wang ◽  
Y. Zhang ◽  
J. Hao ◽  
M. Luo
Keyword(s):  
Yangtze River ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Chemical Transport ◽  
Anthropogenic Emissions ◽  
Spatial Gradient ◽  
Chemical Transport Model ◽  
Significant Drop ◽  
Background Ozone

Abstract. Both observations and a 3-D chemical transport model suggest that surface ozone over populated eastern China features a significant drop in mid-summer and that the peak month differs by latitude and region. Source-receptor analysis is used to quantify the contributions of background ozone and Chinese anthropogenic emissions on this variability. Annual mean background ozone over China shows a spatial gradient from 55 ppbv in the northwest to 20 ppbv in the southeast, corresponding with changes in topography and ozone lifetime. Anthropogenic background (annual mean of 12.6 ppbv) shows distinct troughs in the summer and peaks in the spring. On the monthly-mean basis, Chinese pollution ozone (CPO) has a peak of 20–25 ppbv in June north of the Yangtze River and in October south of it, which explains the peaks of surface ozone in these months. The mid-summer drop in ozone over eastern China is driven by the decrease of background ozone (−15 ppbv). Tagged simulations suggest that this decrease is driven by reduced transport from Europe and North America, whereas ozone from Southeast Asia and Pacific Ocean exhibits a maximum in the summer over eastern China. This contrast in seasonality provides clear evidence that the seasonal switch in monsoonal wind patterns plays a significant role in determining the seasonality of background ozone over China.

scholarly journals Unraveling pathways of elevated ozone induced by the 2020 lockdown in Europe by an observationally constrained regional model using TROPOMI

2021 ◽  
Vol 21 (24) ◽  
pp. 18227-18245
Author(s):  
Amir H. Souri ◽  
Kelly Chance ◽  
Juseon Bak ◽  
Caroline R. Nowlan ◽  
Gonzalo González Abad ◽  
...  
Keyword(s):  
Transport Model ◽  
Atmospheric Transport ◽  
Model Simulation ◽  
Surface Ozone ◽  
Ozone Production ◽  
Anthropogenic Emissions ◽  
Nox Emissions ◽  
Chemical Transport Model ◽  
Voc Emissions ◽  
Constrained Model

Abstract. Questions about how emissions are changing during the COVID-19 lockdown periods cannot be answered by observations of atmospheric trace gas concentrations alone, in part due to simultaneous changes in atmospheric transport, emissions, dynamics, photochemistry, and chemical feedback. A chemical transport model simulation benefiting from a multi-species inversion framework using well-characterized observations should differentiate those influences enabling to closely examine changes in emissions. Accordingly, we jointly constrain NOx and VOC emissions using well-characterized TROPOspheric Monitoring Instrument (TROPOMI) HCHO and NO2 columns during the months of March, April, and May 2020 (lockdown) and 2019 (baseline). We observe a noticeable decline in the magnitude of NOx emissions in March 2020 (14 %–31 %) in several major cities including Paris, London, Madrid, and Milan, expanding further to Rome, Brussels, Frankfurt, Warsaw, Belgrade, Kyiv, and Moscow (34 %–51 %) in April. However, NOx emissions remain at somewhat similar values or even higher in some portions of the UK, Poland, and Moscow in March 2020 compared to the baseline, possibly due to the timeline of restrictions. Comparisons against surface monitoring stations indicate that the constrained model underrepresents the reduction in surface NO2. This underrepresentation correlates with the TROPOMI frequency impacted by cloudiness. During the month of April, when ample TROPOMI samples are present, the surface NO2 reductions occurring in polluted areas are described fairly well by the model (model: −21 ± 17 %, observation: −29 ± 21 %). The observational constraint on VOC emissions is found to be generally weak except for lower latitudes. Results support an increase in surface ozone during the lockdown. In April, the constrained model features a reasonable agreement with maximum daily 8 h average (MDA8) ozone changes observed at the surface (r=0.43), specifically over central Europe where ozone enhancements prevail (model: +3.73 ± 3.94 %, +1.79 ppbv, observation: +7.35 ± 11.27 %, +3.76 ppbv). The model suggests that physical processes (dry deposition, advection, and diffusion) decrease MDA8 surface ozone in the same month on average by −4.83 ppbv, while ozone production rates dampened by largely negative JNO2[NO2]-kNO+O3[NO][O3] become less negative, leading ozone to increase by +5.89 ppbv. Experiments involving fixed anthropogenic emissions suggest that meteorology contributes to 42 % enhancement in MDA8 surface ozone over the same region with the remaining part (58 %) coming from changes in anthropogenic emissions. Results illustrate the capability of satellite data of major ozone precursors to help atmospheric models capture ozone changes induced by abrupt emission anomalies.

scholarly journals Implementation of Yale Interactive terrestrial Biosphere model version 1.0 into GEOS-Chem version 12.0.0: a tool for biosphere-chemistry interactions

2019 ◽  
Author(s):  
Yadong Lei ◽  
Xu Yue ◽  
Hong Liao ◽  
Cheng Gong ◽  
Lin Zhang
Keyword(s):  
Stomatal Conductance ◽  
Atmospheric Chemistry ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Eastern China ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Terrestrial Biosphere ◽  
Area Index

Abstract. The terrestrial biosphere and atmospheric chemistry interact through multiple feedbacks, but the models of vegetation and chemistry are developed separately. In this study, the Yale Interactive terrestrial Biosphere (YIBs) model, a dynamic vegetation model with biogeochemical processes, is implemented into the Chemical Transport Model GEOS-Chem version 12.0.0. Within the GC-YIBs framework, leaf area index (LAI) and canopy stomatal conductance dynamically predicted by YIBs are used for dry deposition calculation in GEOS-Chem. In turn, the simulated surface ozone (O3) by GEOS-Chem affect plant photosynthesis and biophysics in YIBs. The updated stomatal conductance and LAI improve the simulated daytime O3 dry deposition velocity for major tree species. Compared with the GEOS-Chem model, the model-to-observation correlation for dry deposition velocities increases from 0.76 to 0.85 while the normalized mean error decreases from 35 % to 27 % using the GC-YIBs model. Furthermore, we quantify O3 vegetation damaging effects and find a global reduction of annual gross primary productivity by 2–5 %, with regional extremes of 11–15 % in the eastern U.S. and eastern China. The online GC-YIBs model provides a useful tool for discerning the complex feedbacks between atmospheric chemistry and terrestrial biosphere under global change.

scholarly journals Importance of Dry Deposition Parameterization Choice in Global Simulations of Surface Ozone

2019 ◽  
Author(s):  
Anthony Y. H. Wong ◽  
Jeffrey A. Geddes ◽  
Amos P. K. Tai ◽  
Sam J. Silva
Keyword(s):  
Land Cover ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Deposition Velocity ◽  
Chemical Transport Model ◽  
Area Index ◽  
Ozone Sensitivity ◽  
Dry Deposition Velocity ◽  
The Impact

Abstract. Dry deposition is the second largest sink of tropospheric ozone. Increasing evidence has shown that ozone dry deposition actively links meteorology and hydrology with ozone air quality. However, there is little systematic investigation on the performance of different ozone dry deposition parameterizations at the global scale, and how parameterization choice can impact surface ozone simulations. Here we present the results of the first global, multi-decade modelling and evaluation of ozone dry deposition velocity (vd) using multiple ozone dry deposition parameterizations. We use consistent assimilated meteorology and satellite-derived leaf area index (LAI) to simulate vd over 1982–2011 driven by four sets of ozone dry deposition parametrization that are representative of the current approaches of global ozone dry deposition modelling, such that the differences in simulated vd are entirely due to differences in deposition model structures. In addition, we use the surface ozone sensitivity to vd predicted by a chemical transport model to estimate the impact of mean and variability of ozone dry deposition velocity on surface ozone. Our estimated vd from four different parameterizations are evaluated against field observations, and while performance varies considerably by land cover types, our results suggest that none of the parameterizations are universally better than the others. Discrepancy in simulated mean vd among the parameterizations is estimated to cause 2 to 5 ppbv of discrepancy in surface ozone in the Northern Hemisphere (NH) and up to 8 ppbv in tropical rainforest in July, and up to 8 ppbv in tropical rainforests and seasonally dry tropical forests in Indochina in December. Parameterization-specific biases based on individual land cover type and hydroclimate are found to be the two main drivers of such discrepancies. We find statistically significant trends in the multiannual time series of simulated July daytime vd in all parameterizations, driven by warming and drying (southern Amazonia, southern African savannah and Mongolia) or greening (high latitudes). The trends in July daytime vd is estimated to be 1 % yr−1 and leads to up to 3 ppbv of surface ozone changes over 1982–2011. The interannual coefficient of variation (CV) of July daytime mean vd in NH is found to be 5 %–15 %, with spatial distribution that varies with the dry deposition parameterization. Our sensitivity simulations suggest this can contribute between 0.5 to 2 ppbv to interannual variability (IAV) in surface ozone, but all models tend to underestimate interannual CV when compared to long-term ozone flux observations. We also find that IAV in some dry deposition parameterizations are more sensitive to LAI while others are more sensitive to climate. Comparisons with other published estimates of the IAV of background ozone confirm that ozone dry deposition can be an important part of natural surface ozone variability. Our results demonstrate the importance of ozone dry deposition parameterization choice on surface ozone modelling, and the impact of IAV of vd on surface ozone, thus making a strong case for further measurement, evaluation and model-data integration of ozone dry deposition on different spatiotemporal scales.

scholarly journals Meteorological modes of variability for fine particulate matter (PM2.5) air quality in the United States: implications for PM2.5 sensitivity to climate change

2012 ◽  
Vol 12 (6) ◽  
pp. 3131-3145 ◽  
Author(s):  
A. P. K. Tai ◽  
L. J. Mickley ◽  
D. J. Jacob ◽  
E. M. Leibensperger ◽  
L. Zhang ◽  
...  
Keyword(s):  
Climate Change ◽  
General Circulation ◽  
Transport Model ◽  
Circulation Model ◽  
Multiple Linear Regression Model ◽  
The United States ◽  
Chemical Transport Model ◽  
Modes Of Variability ◽  
The Us ◽  
Cyclone Frequency

Abstract. We applied a multiple linear regression model to understand the relationships of PM2.5 with meteorological variables in the contiguous US and from there to infer the sensitivity of PM2.5 to climate change. We used 2004–2008 PM2.5 observations from ~1000 sites (~200 sites for PM2.5 components) and compared to results from the GEOS-Chem chemical transport model (CTM). All data were deseasonalized to focus on synoptic-scale correlations. We find strong positive correlations of PM2.5 components with temperature in most of the US, except for nitrate in the Southeast where the correlation is negative. Relative humidity (RH) is generally positively correlated with sulfate and nitrate but negatively correlated with organic carbon. GEOS-Chem results indicate that most of the correlations of PM2.5 with temperature and RH do not arise from direct dependence but from covariation with synoptic transport. We applied principal component analysis and regression to identify the dominant meteorological modes controlling PM2.5 variability, and show that 20–40% of the observed PM2.5 day-to-day variability can be explained by a single dominant meteorological mode: cold frontal passages in the eastern US and maritime inflow in the West. These and other synoptic transport modes drive most of the overall correlations of PM2.5 with temperature and RH except in the Southeast. We show that interannual variability of PM2.5 in the US Midwest is strongly correlated with cyclone frequency as diagnosed from a spectral-autoregressive analysis of the dominant meteorological mode. An ensemble of five realizations of 1996–2050 climate change with the GISS general circulation model (GCM) using the same climate forcings shows inconsistent trends in cyclone frequency over the Midwest (including in sign), with a likely decrease in cyclone frequency implying an increase in PM2.5. Our results demonstrate the need for multiple GCM realizations (because of climate chaos) when diagnosing the effect of climate change on PM2.5, and suggest that analysis of meteorological modes of variability provides a computationally more affordable approach for this purpose than coupled GCM-CTM studies.

Effects of Dry Deposition on Surface Ozone over South Asia Inferred from a Regional Chemical Transport Model

2020 ◽  
Vol 4 (2) ◽  
pp. 321-327 ◽  
Author(s):  
Amit Sharma ◽  
Narendra Ojha ◽  
Tabish U. Ansari ◽  
Som K. Sharma ◽  
Andrea Pozzer ◽  
...  
Keyword(s):  
South Asia ◽  
Dry Deposition ◽  
Transport Model ◽  
Surface Ozone ◽  
Chemical Transport ◽  
Chemical Transport Model

scholarly journals Regional and intercontinental pollution signatures on modeled and measured PAN at northern mid-latitude mountain sites

2018 ◽  
Author(s):  
Arlene M. Fiore ◽  
Emily V. Fischer ◽  
Shubha Pandey Deolal ◽  
Oliver Wild ◽  
Dan Jaffe ◽  
...  
Keyword(s):  
Transport Model ◽  
Ozone Production ◽  
Anthropogenic Emissions ◽  
Chemical Transport Model ◽  
Reservoir Species ◽  
Remote Troposphere ◽  
Global Chemical Transport Model ◽  
Peroxy Acetyl Nitrate ◽  
Source Receptor Relationships

Abstract. Peroxy acetyl nitrate (PAN) is the most important reservoir species for nitrogen oxides (NOx) in the remote troposphere. Upon decomposition in remote regions, PAN promotes efficient ozone production. We evaluate monthly mean PAN abundances from global chemical transport model simulations (HTAP1) for 2001 with measurements from five northern mid-latitude mountain sites (four European and one North American). The multi-model mean generally captures the observed monthly mean PAN but individual models simulate a factor of ~ 4–8 range in monthly abundances. We quantify PAN source-receptor relationships at the measurement sites with sensitivity simulations that decrease regional anthropogenic emissions of PAN (and ozone) precursors by 20 % from North America (NA), Europe (EU), and East Asia (EA). The HTAP1 models attribute more of the observed PAN at Jungfraujoch (Switzerland) to emissions in NA and EA, and less to EU, than a prior trajectory-based estimate. The trajectory-based and modeling approaches agree that EU emissions play a role in the observed springtime PAN maximum at Jungfraujoch. The signal from anthropogenic emissions on PAN is strongest at Jungfraujoch and Mount Bachelor (Oregon, U.S.A.) during April. In this month, PAN source-receptor relationships correlate both with model differences in regional anthropogenic volatile organic compound (AVOC) emissions and with ozone source-receptor relationships. PAN observations at mountaintop sites can thus provide key information for evaluating models, including links between PAN and ozone production and source-receptor relationships. Establishing routine, long-term, mountaintop measurements is essential given the large observed interannual variability in PAN.

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