Suchergebnisse

High loadings of nitrate ( NO3-) in the aerosol over China significantly exacerbate the air quality and pose a great threat to ecosystem safety through dry–wet deposition. Unfortunately, limited ground-level observation data make it challenging to fully reflect the spatial pattern of NO3- levels across China. Until now, long-term monthly particulate NO3- datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3- dataset at 0.25∘ resolution over China during 2005–2015 was developed by assimilating surface observations, satellite products, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient-boosting decision tree (GBDT), and extreme gradient-boosting (XGBoost) methods. The new developed product featured an excellent cross-validation R2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 µg N m−3) and mean absolute error (MAE: 0.81 µg N m−3). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scales. Moreover, the dataset displayed good agreement with (R2=0.85, RMSE=0.74 µg N m−3, and MAE=0.55 µg N m−3) some unlearned data collected from previous studies. The spatiotemporal variations in the developed product were also shown. The estimated NO3- concentration showed the highest value in the North China Plain (NCP) (3.55±1.25 µg N m−3); followed by the Yangtze River Delta (YRD) (2.56±1.12 µg N m−3), Pearl River Delta (PRD) (1.68±0.81 µg N m−3), and Sichuan Basin (1.53±0.63 µg N m−3), and the lowest one in the Tibetan Plateau (0.42±0.25 µg N m−3). The higher ambient NO3- concentrations in the NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3- pollution in the Sichuan Basin. The lowest ambient NO3- concentration in the Tibetan Plateau was contributed by the scarce anthropogenic emission and favourable meteorological factors (e.g. high wind speed). In addition, the ambient NO3- concentration showed a marked increasing tendency of 0.10 µgNm-3yr-1 during 2005–2014 (p<0.05), while it decreased sharply from 2014 to 2015 at a rate of −0.40 µgNm-3yr-1 (p<0.05). The ambient NO3- levels in Beijing–Tianjin–Hebei (BTH), YRD, and PRD displayed gradual increases at a rate of 0.20, 0.11, and 0.05 µgNm-3yr-1 (p<0.05) during 2005–2013, respectively. The gradual increases in NO3- concentrations in these regions from 2005 to 2013 were due to the fact that the emission reduction measures during this period focused on the reduction of SO2 emission rather than NOx emission and the rapid increase in energy consumption. Afterwards, the government further strengthened these emission reduction measures and thus caused the dramatic decreases in NO3- concentrations in these regions from 2013 to 2015 (p<0.05). The long-term NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005–2015 are open access at https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c).

High loadings of nitrate ( NO3- ) in the aerosol over China significantly exacerbate the air quality and pose a great threat to ecosystem safety through dry–wet deposition. Unfortunately, limited ground-level observation data make it challenging to fully reflect the spatial pattern of NO3- levels across China. Until now, long-term monthly particulate NO3- datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO3- dataset at 0.25 ∘ resolution over China during 2005–2015 was developed by assimilating surface observations, satellite products, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient-boosting decision tree (GBDT), and extreme gradient-boosting (XGBoost) methods. The new developed product featured an excellent cross-validation R 2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 µg N m −3 ) and mean absolute error (MAE: 0.81 µg N m −3 ). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scales. Moreover, the dataset displayed good agreement with ( R 2 =0.85 , RMSE=0.74 µg N m −3 , and MAE=0.55 µg N m −3 ) some unlearned data collected from previous studies. The spatiotemporal variations in the developed product were also shown. The estimated NO3- concentration showed the highest value in the North China Plain (NCP) ( 3.55±1.25 µg N m −3 ); followed by the Yangtze River Delta (YRD) ( 2.56±1.12 µg N m −3 ), Pearl River Delta (PRD) ( 1.68±0.81 µg N m −3 ), and Sichuan Basin ( 1.53±0.63 µg N m −3 ), and the lowest one in the Tibetan Plateau ( 0.42±0.25 µg N m −3 ). The higher ambient NO3- concentrations in the NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO3- pollution in the Sichuan Basin. The lowest ambient NO3- concentration in the Tibetan Plateau was contributed by the scarce anthropogenic emission and favourable meteorological factors (e.g. high wind speed). In addition, the ambient NO3- concentration showed a marked increasing tendency of 0.10 µgNm-3yr-1 during 2005–2014 ( p µgNm-3yr-1 ( p NO3- levels in Beijing–Tianjin–Hebei (BTH), YRD, and PRD displayed gradual increases at a rate of 0.20, 0.11, and 0.05 µgNm-3yr-1 ( p NO3- concentrations in these regions from 2005 to 2013 were due to the fact that the emission reduction measures during this period focused on the reduction of SO 2 emission rather than NO x emission and the rapid increase in energy consumption. Afterwards, the government further strengthened these emission reduction measures and thus caused the dramatic decreases in NO3- concentrations in these regions from 2013 to 2015 ( p NO3- dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO3- levels over China during 2005–2015 are open access at https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c).

High loadings of nitrate (NO 3 − ) in the aerosol over China significantly exacerbates the air quality and poses a great threaten on ecosystem safety through dry/wet deposition. Unfortunately, limited ground-level observation data makes it challenging to fully reflect the spatial pattern of NO 3 − level across China. Up to date, the long-term monthly NO 3 − datasets at a high resolution were still missing, which restricted the assessment of human health and ecosystem safety. Therefore, a unique monthly NO 3 − dataset at 0.25° resolution over China during 2005–2015 was developed by assimilating surface observation, satellite product, meteorological data, land use types and other covariates using an ensemble model combining random forest (RF), gradient boosting decision tree (GBDT), and extreme gradient boosting (XGBoost). The new developed product featured excellent cross-validation R 2 value (0.78) and relatively lower root-mean-square error (RMSE: 1.19 μg/m 3 ) and mean absolute error (MAE: 0.81 μg/m 3 ). Besides, the dataset also exhibited relatively robust performance at the spatial and temporal scale. Moreover, the dataset displayed good agreement with (R 2 = 0.85, RMSE = 0.74 μg/m 3 , and MAE = 0.55 μg/m 3 ) some unlearning data collected from previous studies. The spatiotemporal variations of the developed product were also shown. The estimated NO 3 − concentration showed the highest value in North China Plain (NCP) (3.55 ± 1.25 μg/m 3 ), followed by Yangtze River Delta (YRD (2.56 ± 1.12 g/m 3 )), Pearl River Delta (PRD (1.68 ± 0.81 μg/m 3 )), Sichuan Basin (1.53 ± 0.63 μg/m 3 ), and the lowest one in Tibetan Plateau (0.42 ± 0.25 μg/m 3 ). The higher ambient NO 3 − concentrations in NCP, YRD, and PRD were closely linked to the dense anthropogenic emissions. Apart from the intensive human activities, poor terrain condition might be a key factor for the serious NO 3 − pollution in Sichuan Basin. The lowest ambient NO 3 − concentration in Tibetan Plateau was contributed by the scarce anthropogenic emission and favorable meteorological factors (e.g., high wind speed). In addition, the ambient NO 3 − concentration showed marked increasing tendency of 0.10 μg/m 3 /year during 2005–2014 ( p < 0.05), while it decreased sharply from 2014 to 2015 at a speed of −0.40 μg/m 3 /year ( p < 0.05). The ambient NO 3 − levels in Beijing-Tianjin-Hebei (BTH), YRD, and PRD displayed gradual increases at the speed of 0.13, 0.08, and 0.03 μg/m 3 /year ( p < 0.05) during 2005–2014, respectively. The gradual increases of NO 3 − concentrations in these regions from 2005 to 2014 were due to that the emission reduction measures during this period focused on the reduction of SO 2 emission rather than NO x emission and the rapid increase of energy consumption. Afterwards, the government further strengthened these emission reduction measures, and thus caused the dramatic decreases of NO 3 − concentrations in these regions from 2014 to 2015 ( p < 0.05). The long-term NO 3 − dataset over China could greatly deepen the knowledge about the impacts of emission reduction measures on air quality improvement. The monthly particulate NO 3 − levels over China during 2005–2015 are open access in https://doi.org/10.5281/zenodo.3988307 (Li et al., 2020c).

The increasing ozone (O 3 ) pollution and high fraction of secondary organic aerosols (SOA) in fine particle mass highlighted the importance of volatile organic compounds (VOCs) in air pollution control. In this work, a campaign of comprehensive field observations was conducted at an urban site in Beijing, from December 2018 to November 2019, to identify the composition, sources, and secondary transformation potential of VOCs. The total mixing ratio of the 95 quantified VOCs (TVOC) observed in this study ranged from 5.5–118.7 ppbv with the mean value of 34.9 ppbv, and the contemporaneous mixing ratios of TVOC was significantly lower than those observed in 2014 and 2016, confirming the effectiveness of VOCs emission control measures in Beijing in recent years. Alkanes, OVOCs and halocarbons were the dominant chemical groups, accounting for 75–81 % of the TVOCs across the sampling months. High and low-O 3 /PM 2.5 months as well as several O 3 /PM 2.5 polluted days were identified during the sampling period. By deweathered calculation, we found that high O 3 /PM 2.5 levels were due to both enhanced precursor emission levels and meteorological conditions favorable to O 3 and PM 2.5 production. The molar ratios of VOCs to NO X indicated that O 3 formation was limited by VOCs during the whole sampling period. Diesel exhaust and industrial emission were identified as the major VOCs sources on both O 3 -polluted and PM 2.5 -polluted days based on positive matrix factorization (PMF) analysis, accounting for 46 % and 53 %, respectively. Moreover, higher proportion of oil/gas evaporation was observed on O 3 -polluted days (18 %) than that on O 3 -clean days (13 %), and higher proportion of coal/biomass combustion was observed on PM 2.5 -polluted days (18 %) than that on PM 2.5 -clean days (13 %). On the base of O 3 formation impact, VOCs from fuel evaporation and diesel exhaust particularly toluene, xylenes, trans-2-butene, acrolein, methyl methacrylate, vinyl acetate, 1-butene and 1-hexene were the main contributors, illustrating the necessity of conducting emission controls on these pollution sources and species for alleviating O 3 pollution. Instead, VOCs from diesel exhaust and coal/biomass combustion were found to be the dominant contributors for secondary organic aerosol formation potential (SOAFP), particularly the VOC species of toluene, 1-hexene, xylenes, ethylbenzene and styrene, and top priority should be given to these for the alleviation of haze pollution. The positive matrix factorization (PSCF) analysis showed that O 3 and PM 2.5 pollution was mainly affected by local emissions. This study provides insights for government to formulate effective VOCs control measures for air pollution in Beijing.