scholarly journals Revisiting the contribution of land transport and shipping emissions to tropospheric ozone

2018 ◽  
Vol 18 (8) ◽  
pp. 5567-5588 ◽  
Author(s):  
Mariano Mertens ◽  
Volker Grewe ◽  
Vanessa S. Rieger ◽  
Patrick Jöckel

Abstract. We quantify the contribution of land transport and shipping emissions to tropospheric ozone for the first time with a chemistry–climate model including an advanced tagging method (also known as source apportionment), which considers not only the emissions of nitrogen oxides (NOx, NO, and NO2), carbon monoxide (CO), and volatile organic compounds (VOC) separately, but also their non-linear interaction in producing ozone. For summer conditions a contribution of land transport emissions to ground-level ozone of up to 18 % in North America and Southern Europe is estimated, which corresponds to 12 and 10 nmol mol−1, respectively. The simulation results indicate a contribution of shipping emissions to ground-level ozone during summer on the order of up to 30 % in the North Pacific Ocean (up to 12 nmol mol−1) and 20 % in the North Atlantic Ocean (12 nmol mol−1). With respect to the contribution to the tropospheric ozone burden, we quantified values of 8 and 6 % for land transport and shipping emissions, respectively. Overall, the emissions from land transport contribute around 20 % to the net ozone production near the source regions, while shipping emissions contribute up to 52 % to the net ozone production in the North Pacific Ocean. To put these estimates in the context of literature values, we review previous studies. Most of them used the perturbation approach, in which the results for two simulations, one with all emissions and one with changed emissions for the source of interest, are compared. For a better comparability with these studies, we also performed additional perturbation simulations, which allow for a consistent comparison of results using the perturbation and the tagging approach. The comparison shows that the results strongly depend on the chosen methodology (tagging or perturbation approach) and on the strength of the perturbation. A more in-depth analysis for the land transport emissions reveals that the two approaches give different results, particularly in regions with large emissions (up to a factor of 4 for Europe). Our estimates of the ozone radiative forcing due to land transport and shipping emissions are, based on the tagging method, 92 and 62 mW m−2, respectively. Compared to our best estimates, previously reported values using the perturbation approach are almost a factor of 2 lower, while previous estimates using NOx-only tagging are almost a factor of 2 larger. Overall our results highlight the importance of differentiating between the perturbation and the tagging approach, as they answer two different questions. In line with previous studies, we argue that only the tagging approach (or source apportionment approaches in general) can estimate the contribution of emissions, which is important to attribute emission sources to climate change and/or extreme ozone events. The perturbation approach, however, is important to investigate the effect of an emission change. To effectively assess mitigation options, both approaches should be combined. This combination allows us to track changes in the ozone production efficiency of emissions from sources which are not mitigated and shows how the ozone share caused by these unmitigated emission sources subsequently increases.

2017 ◽  
Author(s):  
Mariano Mertens ◽  
Volker Grewe ◽  
Vanessa S. Rieger ◽  
Patrick Jöckel

Abstract. We quantify the contribution of land transport and shipping emissions to tropospheric ozone for the first time with a chemistry-climate model including an advanced tagging method, which considers not only the emissions of NOx (NO and NO2), CO or non-methane hydrocarbons (NMHC) separately but the competing effects of all relevant ozone precursors. For summer conditions a contribution of land transport emissions to ground level ozone of up to 18 % in North America and South Europe is estimated, which corresponds to 12 nmol mol−1 and 10 nmol mol−1, respectively. The simulation results indicate a contribution of shipping emissions to ground level ozone during summer in the order of up to 30 % in the Northern Pacific (up to 12 nmol mol−1) and 20 % in the Northern Atlantic (12 nmol mol−1). To put these estimates in the context of literature values, we review previous studies. Most of them used the perturbation approach, in which the results from two simulations, one with all emissions and one with changed emissions for the source of interest, are compared. The comparison shows that the results strongly depend on the chosen methodology (tagging or perturbation method) and on the strength of the perturbation. A more in-depth analysis for the land transport emissions reveals that the two approaches give different results particularly in regions with large emissions (up to a factor of four for Europe). With respect to the contribution of land transport and ship traffic emissions to the tropospheric ozone burden we quantified values of 8 % and 6 % for the land transport and shipping emissions, respectively. Overall, the emissions from land transport contribute to around 20 % of the net ozone production near the source regions, while shipping emissions contribute up to 52 % to the net ozone production in the Northern Pacific. Our estimates of the radiative ozone forcing due to emissions of land transport and shipping emissions are 92 mW m−2 and 62 mW m−2, respectively. Again these results are larger by a factor of 2–3 compared to previous studies using the perturbation approach, but largely agree with previous studies which investigated the difference between the tagging and the perturbation method. Overall our results highlight the importance of differing between the perturbation and the tagging approach, as they answer two different questions. We argue that only the tagging approach can estimate the contribution of emissions, while only the perturbation approach investigates the effect of an emission change. To effectively asses mitigation options both approaches should be combined.


2019 ◽  
Author(s):  
Mariano Mertens ◽  
Astrid Kerkweg ◽  
Volker Grewe ◽  
Patrick Jöckel ◽  
Robert Sausen

Abstract. Anthropogenic and natural emissions influence the tropospheric ozone budget, thereby affecting air-quality and climate. To study the influence of different emission sources on the ozone budget, often source apportionment studies with a tagged tracer approach are performed. Studies investigating air quality issues usually rely on regional models with a high spatial resolution, while studies focusing on climate related questions often use coarsely resolved global models. It is well known that simulated ozone concentrations depend on the resolution of the model and the resolution of the emission inventory. Whether the contributions simulated by source apportionment approaches also depend on the model resolution, however, is still unclear. Therefore, this study is a first attempt to analyse the impact of the model, the model resolution, and the emission inventory resolution on simulated ozone contributions diagnosed with a tagging method. The differences of the ozone contributions caused by these factors are compared with differences which arise due to different emission inventories. To do so we apply the MECO(n) model system which on-line couples a global chemistry-climate model with a regional chemistry-climate model equipped with a tagging scheme for source apportionment. The results of the global model (300 km resolution) are compared with the results of the regional model at 50 km (Europe) and 12 km (Germany) resolution. Averaged over Europe the simulated contributions of land transport emissions to ground-level ozone differ by 10 % at maximum. For other anthropogenic emission sources the differences are in the same order of magnitude, while the contribution of stratospheric ozone to ground level ozone differs by up to 30 % on average. This suggests that ozone contributions of anthropogenic emission sources averaged on continental scale are rather robust with respect to different models, model and emission inventory resolutions. On regional scale, however, we quantified differences of the contribution of land transport emissions to ozone of up to 20 %. Depending on the region the largest differences are either caused by inter model differences, or differences of the anthropogenic emission inventories. Clearly, the results strongly depend on the compared models and emission inventories and cannot necessarily be generalised, however we show how the inclusion of source apportionment methods can help in analysing inter-model differences.


Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 388
Author(s):  
Hao Cheng ◽  
Liang Sun ◽  
Jiagen Li

The extraction of physical information about the subsurface ocean from surface information obtained from satellite measurements is both important and challenging. We introduce a back-propagation neural network (BPNN) method to determine the subsurface temperature of the North Pacific Ocean by selecting the optimum input combination of sea surface parameters obtained from satellite measurements. In addition to sea surface height (SSH), sea surface temperature (SST), sea surface salinity (SSS) and sea surface wind (SSW), we also included the sea surface velocity (SSV) as a new component in our study. This allowed us to partially resolve the non-linear subsurface dynamics associated with advection, which improved the estimated results, especially in regions with strong currents. The accuracy of the estimated results was verified with reprocessed observational datasets. Our results show that the BPNN model can accurately estimate the subsurface (upper 1000 m) temperature of the North Pacific Ocean. The corresponding mean square errors were 0.868 and 0.802 using four (SSH, SST, SSS and SSW) and five (SSH, SST, SSS, SSW and SSV) input parameters and the average coefficients of determination were 0.952 and 0.967, respectively. The input of the SSV in addition to the SSH, SST, SSS and SSW therefore has a positive impact on the BPNN model and helps to improve the accuracy of the estimation. This study provides important technical support for retrieving thermal information about the ocean interior from surface satellite remote sensing observations, which will help to expand the scope of satellite measurements of the ocean.


2021 ◽  
Author(s):  
R. J. David Wells ◽  
Veronica A. Quesnell ◽  
Robert L. Humphreys ◽  
Heidi Dewar ◽  
Jay R. Rooker ◽  
...  

Author(s):  
An Zhang ◽  
Jinhuang Lin ◽  
Wenhui Chen ◽  
Mingshui Lin ◽  
Chengcheng Lei

Long-term exposure to ozone pollution will cause severe threats to residents’ physical and mental health. Ground-level ozone is the most severe air pollutant in China’s Pearl River Delta Metropolitan Region (PRD). It is of great significance to accurately reveal the spatial–temporal distribution characteristics of ozone pollution exposure patterns. We used the daily maximum 8-h ozone concentration data from PRD’s 55 air quality monitoring stations in 2015 as input data. We used six models of STK and ordinary kriging (OK) for the simulation of ozone concentration. Then we chose a better ozone pollution prediction model to reveal the ozone exposure characteristics of the PRD in 2015. The results show that the Bilonick model (BM) model had the highest simulation precision for ozone in the six models for spatial–temporal kriging (STK) interpolation, and the STK model’s simulation prediction results are significantly better than the OK model. The annual average ozone concentrations in the PRD during 2015 showed a high spatial variation in the north and east and low in the south and west. Ozone concentrations were relatively high in summer and autumn and low in winter and spring. The center of gravity of ozone concentrations tended to migrate to the north and west before moving to the south and then finally migrating to the east. The ozone’s spatial autocorrelation was significant and showed a significant positive correlation, mainly showing high-high clustering and low-low clustering. The type of clustering undergoes temporal migration and conversion over the four seasons, with spatial autocorrelation during winter the most significant.


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