46 research outputs found

    Impact of Legislated and Best Available Emission Control Measures on UK Particulate Matter Pollution, Premature Mortality, and Nitrogen-Sensitive Habitats

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    Past emission controls in the UK have substantially reduced precursor emissions of health-hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5 μg m-3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS-Chem model, high-resolution data sets, and contemporary exposure-risk relationships to assess potential human and ecosystem health co-benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present-day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188-52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats

    Diagnosing domestic and transboundary sources of fine particulate matter (PM2.5) in UK cities using GEOS-Chem

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    The UK is set to impose a stricter ambient annual mean fine particulate matter (PM2.5) standard than was first adopted fourteen years ago. This necessitates strengthened knowledge of the magnitude and sources that influence urban PM2.5 in UK cities to ensure compliance and improve public health. Here, we use a regional-scale chemical transport model (GEOS-Chem), validated with national ground-based observations, to quantify the influence of specific sources within and transported to the mid-sized UK city Leicester. Of the sources targeted, we find that agricultural emissions of ammonia (NH3) make the largest contribution (3.7 μg m−3 or 38 % of PM2.5) to annual mean PM2.5 in Leicester. Another important contributor is long-range transport of pollution from continental Europe accounting for 1.8 μg m−3 or 19 % of total annual mean PM2.5. City sources are a much smaller portion (0.2 μg m−3; 2 %). We also apply GEOS-Chem to the much larger cities Birmingham and London to find that agricultural emissions of NH3 have a greater influence than city sources for Birmingham (32 % agriculture, 19 % city) and London (25 % agriculture, 13 % city). The portion from continental Europe is 16 % for Birmingham and 28 % for London. Action plans aimed at national agricultural sources of NH3 and strengthened supranational agreements would be most effective at alleviating PM2.5 in most UK cities

    Evaluation of the WRF and CHIMERE models for the simulation of PMâ‚‚.â‚… in large East African urban conurbations

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    Urban conurbations of East Africa are affected by harmful levels of air pollution. The paucity of local air quality networks and the absence of the capacity to forecast air quality make difficult to quantify the real level of air pollution in this area. The CHIMERE chemistry transport model has been used along with the Weather Research and Forecasting (WRF) meteorological model to run high-spatial-resolution (2 × 2 km) simulations of hourly concentrations of particulate matter with an aerodynamic diameter smaller than 2.5 µm (PM2.5) for three East African urban conurbations: Addis Ababa in Ethiopia, Nairobi in Kenya, and Kampala in Uganda. Two existing emission inventories were combined to test the performance of CHIMERE as an air quality model for a target monthly period in 2017, and the results were compared against observed data from urban, roadside, and rural sites. The results show that the model is able to reproduce hourly and daily temporal variabilities in aerosol concentrations that are close to observed values from urban, roadside, and rural environments. CHIMERE's performance as a tool for managing air quality was also assessed. The analysis demonstrated that, despite the absence of high-resolution data and up-to-date biogenic and anthropogenic emissions, the model was able to reproduce 66 %–99 % of the daily PM2.5 exceedances above the World Health Organization (WHO) 24 h mean PM2.5 guideline (25 µg m−3) in the three cities. An analysis of the 24 h average PM2.5 levels was also carried out for 17 constituencies in the vicinity of Nairobi. This showed that 47 % of the constituencies in the area exhibited a poor Air Quality Index for PM2.5 that was in the unhealthy category for human health, thereby exposing between 10 000 and 30 000 people per square kilometre to harmful levels of air contamination

    Sensitivity to grid resolution in the ability of a chemical transport model to simulate observed oxidant chemistry under high-isoprene conditions

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    Formation of ozone and organic aerosol in continental atmospheres depends on whether isoprene emitted by vegetation is oxidized by the high-NOx pathway (where peroxy radicals react with NO) or by low-NOx pathways (where peroxy radicals react by alternate channels, mostly with HO2). We used mixed layer observations from the SEAC4RS aircraft campaign over the Southeast US to test the ability of the GEOS-Chem chemical transport model at different grid resolutions (0.25°  ×  0.3125°, 2°  ×  2.5°, 4°  ×  5°) to simulate this chemistry under high-isoprene, variable-NOx conditions. Observations of isoprene and NOx over the Southeast US show a negative correlation, reflecting the spatial segregation of emissions; this negative correlation is captured in the model at 0.25°  ×  0.3125° resolution but not at coarser resolutions. As a result, less isoprene oxidation takes place by the high-NOx pathway in the model at 0.25°  ×  0.3125° resolution (54 %) than at coarser resolution (59 %). The cumulative probability distribution functions (CDFs) of NOx, isoprene, and ozone concentrations show little difference across model resolutions and good agreement with observations, while formaldehyde is overestimated at coarse resolution because excessive isoprene oxidation takes place by the high-NOx pathway with high formaldehyde yield. The good agreement of simulated and observed concentration variances implies that smaller-scale non-linearities (urban and power plant plumes) are not important on the regional scale. Correlations of simulated vs. observed concentrations do not improve with grid resolution because finer modes of variability are intrinsically more difficult to capture. Higher model resolution leads to decreased conversion of NOx to organic nitrates and increased conversion to nitric acid, with total reactive nitrogen oxides (NOy) changing little across model resolutions. Model concentrations in the lower free troposphere are also insensitive to grid resolution. The overall low sensitivity of modeled concentrations to grid resolution implies that coarse resolution is adequate when modeling continental boundary layer chemistry for global applications

    Impact of legislated and best available emission control measures on UK particulate matter pollution, premature mortality, and nitrogen-sensitive habitats

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    Past emission controls in the UK have substantially reduced precursor emissions of health-hazardous fine particles (PM2.5) and nitrogen pollution detrimental to ecosystems. Still, 79% of the UK exceeds the World Health Organization (WHO) guideline for annual mean PM2.5 of 5 μg m−3 and there is no enforcement of controls on agricultural sources of ammonia (NH3). NH3 is a phytotoxin and an increasingly large contributor to PM2.5 and nitrogen deposited to sensitive habitats. Here we use emissions projections, the GEOS-Chem model, high-resolution data sets, and contemporary exposure-risk relationships to assess potential human and ecosystem health co-benefits in 2030 relative to the present day of adopting legislated or best available emission control measures. We estimate that present-day annual adult premature mortality attributable to exposure to PM2.5 is 48,625 (95% confidence interval: 45,188–52,595), that harmful amounts of reactive nitrogen deposit to almost all (95%) sensitive habitat areas, and that 75% of ambient NH3 exceeds levels safe for bryophytes and lichens. Legal measures decrease the extent of the UK above the WHO guideline to 58% and avoid 6,800 premature deaths by 2030. This improves with best available measures to 36% of the UK and 13,300 avoided deaths. Both legal and best available measures are insufficient at reducing the extent of damage of nitrogen pollution to sensitive habitats. Far more ambitious reductions in nitrogen emissions (>80%) than is achievable with best available measures (34%) are required to halve the amount of excess nitrogen deposited to sensitive habitats

    Why do models overestimate surface ozone in the Southeast United States

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    Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx  ≡  NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25°  ×  0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer

    Why do Models Overestimate Surface Ozone in the Southeastern United States?

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    Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx = NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25 deg. x 0.3125 deg. horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30-60%, dependent on the assumption of the contribution by soil NOx emissions. Upper tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft, and reproduces the observed ozone production efficiency in the boundary layer as derived from a 15 regression of ozone and NOx oxidation products. However, the model is still biased high by 8 +/- 13 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer

    New observations of NO2 in the upper troposphere from TROPOMI

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    Nitrogen oxides (NOx≡NO+NO2) in the NOx-limited upper troposphere (UT) are long-lived and so have a large influence on the oxidizing capacity of the troposphere and formation of the greenhouse gas ozone. Models misrepresent NOx in the UT, and observations to address deficiencies in models are sparse. Here we obtain a year of near-global seasonal mean mixing ratios of NO2 in the UT (450–180 hPa) at 1∘×1∘ by applying cloud-slicing to partial columns of NO2 from TROPOMI. This follows refinement of the cloud-slicing algorithm with synthetic partial columns from the GEOS-Chem chemical transport model. TROPOMI, prior to cloud-slicing, is corrected for a 13 % underestimate in stratospheric NO2 variance and a 50 % overestimate in free-tropospheric NO2 determined by comparison to Pandora total columns at high-altitude free-tropospheric sites at Mauna Loa, Izaña, and Altzomoni and MAX-DOAS and Pandora tropospheric columns at Izaña. Two cloud-sliced seasonal mean UT NO2 products for June 2019 to May 2020 are retrieved from corrected TROPOMI total columns using distinct TROPOMI cloud products that assume clouds are reflective boundaries (FRESCO-S) or water droplet layers (ROCINN-CAL). TROPOMI UT NO2 typically ranges from 20–30 pptv over remote oceans to >80 pptv over locations with intense seasonal lightning. Spatial coverage is mostly in the tropics and subtropics with FRESCO-S and extends to the midlatitudes and polar regions with ROCINN-CAL, due to its greater abundance of optically thick clouds and wider cloud-top altitude range. TROPOMI UT NO2 seasonal means are spatially consistent (R=0.6–0.8) with an existing coarser spatial resolution (5∘ latitude × 8∘ longitude) UT NO2 product from the Ozone Monitoring Instrument (OMI). UT NO2 from TROPOMI is 12–26 pptv more than that from OMI due to increase in NO2 with altitude from the OMI pressure ceiling (280 hPa) to that for TROPOMI (180 hPa), but possibly also due to altitude differences in TROPOMI and OMI cloud products and NO2 retrieval algorithms. The TROPOMI UT NO2 product offers potential to evaluate and improve representation of UT NOx in models and supplement aircraft observations that are sporadic and susceptible to large biases in the UT.This research has been supported by the European Research Council under the European Union’s Horizon 2020 research and innovation programme (through the Starting Grant awarded to Eloise A. Marais, UpTrop (grant no. 851854))
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