Ozone (O3) pollution is becoming increasingly severe in China. In addition, our limited understanding of the relationship between O3 and volatile organic compounds (VOCs), is an obstacle to improving air quality. By developing an improved source-oriented speciated VOC emission inventory in 2013, we estimated the ozone formation potential (OFP) and investigated its characteristics in China. Besides, a comparison was made between our estimates and space-based observations from the ozone monitoring instrument (OMI) on the National Aeronautics and Space Administration (NASA)’s Aura satellite. According to our estimates, m-/p-xylene, ethylene, formaldehyde, toluene, and propene were the five species that had the largest potential to form ozone, and on-road vehicles, industrial processes, biofuel combustion, and surface coating were the key contributing sectors. Among different regions of China, the North China Plain, Yangtze River Delta, and Pearl River Delta had the highest OFP values. Our results suggest that O3formation is VOC-limited in major urban areas of China. Additionally, considering the different photochemical reactivities of various VOC species and the disparate energy and industry structures in the different regions of China, more efficient OFP-based and localized VOC control measures should be implemented, instead of the current mass-based and nationally uniform policies.

Bibliometric analysis was applied to identify global patterns and trends in the research of biogenic volatile organic compounds (BVOCs), which are important to atmospheric ozone formation and secondary organic aerosol formation. Yearly publications, mainstream subject categories and journals, leading countries and institutions, research emphases and trends were identified. Number of publications and times of citation were used as indicators to evaluate publication performances. A summary of the most frequently used keywords obtained from author keywords and KeyWords Plus provided clues for research emphases in different periods. A network of keywords was drawn to visualize the cross-relationship of keywords. Results indicated that annual output of the related scientific papers increased notably during 1991–2014. Atmospheric Sciences, Environmental Sciences & Ecology, Environmental Sciences & Engineering, and Chemistry were the main subject categories. Journal of Geophysical Research-Atmospheres was the most competitive journal in productivity and academic impact. The USA and the National Center for Atmospheric Research (NCAR) were, respectively, the leading country and leading institution in BVOC research. “Emissions,” “isoprene,” and “model” were the leading research emphases in BVOC field in terms of word frequencies and centrality driven from the network structure. The three leading research hotspots cross-fields, emissions-isoprene, emissions-model, and isoprene-model showed substantial growth in scientific outputs during the study period. These trends were evidenced by the evolution of research contents in various studies.

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25° × 0.25° and a temporal resolutionof 1 month was established based on the moderate resolution imaging spectroradiometer(MODIS) Thermal Anomalies/Fire Daily Level3 Global Product (MOD/MYD14A1). Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4, nonmethane volatile organic compounds (NMVOCs), N2O, NOx, NH3, SO2, fine particles (PM2.5), organic carbon (OC), and black carbon (BC) were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43, 1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June (37%). Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ± 126% for N2O to a high of within ± 169% for NH3.

108 ambient volatile organic compounds (VOCs) were measured continuously at a time resolution of an hour using an online gas chromatography–frame ionization detector/mass spectrometry (GC–FID/MS) in October 2014 in Beijing, and positive matrix factorization (PMF) was performed with online data. The evolution process and causes for high levels of VOCs during a haze event were investigated through comprehensive analysis. Results show that mixing ratios of VOCs during the haze event (89.29 ppbv) were 2 to 5 times as that in non-haze days, There was a distinct accumulation process of VOCs at the beginning of the haze event, and the mixing ratios of VOCs maintained at the high levels until to the end of pollution when the mixing ratios of ambient VOCs recovered to the normal concentration levels in a few hours. Some reactive and toxic species increased remarkably as well, which indicates a potential health risk to the public in terms of VOCs. Eight sources were resolved by PMF, and results revealed gasoline exhaust was the largest contributor (32–46%) to the ambient VOCs in Beijing. Emissions of gasoline exhaust surged from 13.46 to 40.36 ppbv, with a similar variation pattern to total VOCs, indicating that high levels of VOCs were largely driven to by expanded vehicular emissions. Emissions of biomass burning also increased noticeably (from 2.32 to 11.12 ppbv), and backward trajectories analysis indicated regional transport of biomass burning emissions. Our findings suggested that extremely high levels of VOCs during the haze event was primarily attributed to vehicular emissions, biomass burning and regional transport, as well as stationary synoptic conditions.

High-resolution historical emission inventories of crop residue burning in fields in China were developed for the period 1990–2013. More accurate time-varying statistical data and locally observed emission factors were utilized to estimate crop residue open burning emissions at provincial level. Then pollutants emissions were allocated to a high spatial resolution of 10 km × 10 km and a high temporal resolution of 1 day based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Fire Product (MOD/MYD14A1). Results show that China’s CO emissions have increased by 5.67 times at an annual average rate of 24% from 1.06 Tg in 1990 to 7.06 Tg in 2013; the emissions of CO2, CH4, NMVOCs, N2O, NOx, NH3, SO2, PM2.5, OC, and BC have increased by 595%, 500%, 608%, 584%, 600%, 600%, 543%, 571%, 775%, and 500%, respectively, over the past 24 years. Spatially, the regions with high emissions had been notable expanding over the years, especially in the central eastern districts, the Northeastern of China, and the Sichuan Basin. Strong temporal pattern were observed with the highest emissions in June, followed by March to May and October. This work provides a better understanding of the spatiotemporal representation of agricultural fire emissions in China and can benefit both air quality modeling and management with improved accuracy.

This paper applies CALPUFF model to simulate the spatial distribution of sulfur dioxide in Urumqi and analyzes the source contribution to areas where the SO2 concentration is high. The result shows that annual mean concentration is highest in the middle of Saybagh and with the value of 44 μg/m3. The maximum 24-h averaged SO2 concentration is highest in the junction area of the middle-west of Saybagh and the north of Urumqi county, and the highest value is 467 μg/m3. The spatial distribution of SO2 in January is similar to that in October, and April is similar to that in July. National monitoring stations are dense in the middle of city where the concentration is low and can't reflect the spatial distribution effectively. Baosteel group contributes most to the Saybagh high concentration area (37 μg/m3/a); China National Petroleum Corporation Urumqi petrochemical company contributes most to the Midong high concentration area (5.3 μg/m3/a); Houxia power plant contributes most to the Houxia high concentration area (33 μg/m3/a).

Chengdu is an inland megacity in the Sichuan Basin, where dust influence remained an open question. During a one-year haze campaign, two dust events were identified in March 2013, indicating that desert dust can be transported to Chengdu and impacted local air quality strongly. The suggested low SO2/PM10, NO2/PM10 and PM2.5/PM10 ratios of 0.15, 0.27 and 0.40 could be used as immediate indicators for dust days. On typical dust day of March 12, PM10 was as high as 359.1 μg m−3, and crustal matter contributed 80.5% to total PM2.5 mass (106.6 μg m−3). Enrichment factors of most elements have decreased due to the dilution effect except for Ca and Mg. The dust was mainly from western and northern dust regions in China, including the “Northerly Mongolia Path”, “Western Desert Path” and “Northwestern Desert Path”. Due to the obstruction of Qinghai-Tibet Plateau on the west, the dust air to Chengdu was mostly from the northeastward direction after passing over Qinling Mountain. Moreover, the air experienced obvious elevation from its source regions driven by the cold front synoptic pattern. The spatial distribution of high AOD (Aerosol Optical Depth) values over 1.2 but low Ångström exponent of 0.5–0.6 around Chengdu verified the coarse pollution patterns. However, the dust pollution was not serious in nearby Chongqing and Guizhou and exhibited weak regional feature, a result different from those in Beijing and Shanghai.

To better understand the chemical speciation of volatile organic compounds (VOCs) and their role in ground-level ozone formation in the Beijing-Tianjin-Hebei region, China, measurements of 56 non-methane hydrocarbons (NMHCs) and 12 carbonyls were conducted at three sites in summer. Alkanes were the largest group of NMHCs (>50%), followed by alkenes and aromatics. Acetone was the most abundant carbonyl species (>50%). The OH loss rates (LOH) of VOCs were calculated to estimate their chemical reactivities. Alkenes played a predominant role in VOC reactivity, among which ethene and propene were the largest contributors. Isoprene contributed 11.61–38.00% of the total reactivity of measured VOCs. Alkenes and aromatics were the largest contributors (47.65–61.53% totally) to the total Ozone Formation Potential (OFP) of measured VOCs based on the observed mixing ratio. Isoprene was the most reactive species, but originated mainly from biogenic emissions. Ethene, m/p-xylene, toluene, propene, o-xylene, and 1-butene were considered to play significant roles in ground-level ozone formation in this region. The OFPs of total measured NMHCs increased by 10.20–22.05% when they were calculated based on the initial mixing ratio. Photochemical losses of hydrocarbons and the secondary formation of carbonyls in this region were also determined. Vehicle exhaust emissions contributed substantially to ambient VOCs.

Ambient volatile organic compounds (VOCs) were measured using an online system, gas chromatography–mass spectrometry/flame ionization detector (GC-MS/FID), in Beijing, China, before, during, and after Asia-Pacific Economic Cooperation (APEC) China 2014, when stringent air quality control measures were implemented. Positive matrix factorization (PMF) was applied to identify the major VOC contributing sources and their temporal variations. The secondary organic aerosols potential (SOAP) approach was used to estimate variations of precursor source contributions to SOA formation. The average VOC mixing ratios during the three periods were 86.17, 48.28, and 72.97 ppbv, respectively. The mixing ratios of total VOC during the control period were reduced by 44 %, and the mixing ratios of acetonitrile, halocarbons, oxygenated VOCs (OVOCs), aromatics, acetylene, alkanes, and alkenes decreased by approximately 65, 62, 54, 53, 37, 36, and 23 %, respectively. The mixing ratios of all measured VOC species decreased during control, and the most affected species were chlorinated VOCs (chloroethane, 1,1-dichloroethylene, chlorobenzene). PMF analysis indicated eight major sources of ambient VOCs, and emissions from target control sources were clearly reduced during the control period. Compared with the values before control, contributions of vehicular exhaust were most reduced, followed by industrial manufacturing and solvent utilization. Reductions of these three sources were responsible for 50, 26, and 16 % of the reductions in ambient VOCs. Contributions of evaporated or liquid gasoline and industrial chemical feedstock were slightly reduced, and contributions of secondary and long-lived species were relatively stable. Due to central heating, emissions from fuel combustion kept on increasing during the whole campaign; because of weak control of liquid petroleum gas (LPG), the highest emissions of LPG occurred in the control period. Vehicle-related sources were the most important precursor sources likely responsible for the reduction in SOA formation during this campaign.

To track the chemical characteristics and formation mechanism of biomass burning pollution, the hourly variations of meteorological factors and pollutant concentrations during a heavy pollution on 18–21 May, 2012 in Chengdu are presented in this study. The episode was the heaviest and most long-lasting pollution event in the historical record of Chengdu caused by a combination of stagnant dispersion conditions and enhanced PM2.5 emission from intensive biomass burning, with peak values surpassing 500 μg m− 3. The event was characterized by three nighttime peaks, relating to the burning practice and decreased boundary layer height at night. The prevailing northeasterly wind during nighttime preferentially brought more pollutants to the urban regions from northern suburbs of Chengdu, where dense fire spots were observed. Due to the obstruction of hilly topography and weak wind speed, minor regional features were reflected from the PM10 variations in nearby cities, whereas the long-distance transport of the plume impacted extensive regions in northern and eastern China. Carbon monoxide (CO) concentrations increased by more than 200%, while exceptionally high PM2.5 levels of 190.1 and 268.4 μg m− 3 on 17 May and 18 May, were observed and showed high correlation with CO (r = 0.75). The relative contribution of biomass burning smoke to organic carbon was estimated from OC/EC ratios (organic carbon/elemental carbon) and elevated to 81.3% during the episode, indicating a significant impact on urban aerosol levels. The occurrence of high PM2.5/PM10 ratios (> 0.80) and K+/EC ratios (> 1.0), along with the increased carbonaceous concentrations and their fraction in PM2.5 (> 40%) and high OC/EC ratios (about 8), could be used as immediate indicators for biomass burning pollution in cities. In addition, the heavy pollution involved a mixture of anthropogenic sources, reflected from the high SOR and NOR values and increases in the EFs (enrichment factors) of Mo, Zn, Cd, and Pb.

The Sichuan Basin is a low visibility area in southwest China, where the hilly and basin topography, plus humid and stagnant weather, lead to unique pollution patterns. To identify the characteristics and sources of carbonaceous aerosols, one-year record of 24-h PM2.5samples were analyzed for organic carbon (OC) and elemental carbon (EC) content following the thermal/optical transmission protocol at three cities (Chengdu (CD), Neijiang (NJ), and Chongqing (CQ)) in the region during May 2012 to April 2013. The annual average concentrations were 19.0 ± 13.3 μg OC m−3 and 4.6 ± 2.6 μg EC m−3 in CD, 18.3 ± 8.4 μg OC m−3 and 4.1 ± 1.8 μg EC m−3 in NJ, and 15.2 ± 8.4 μg OC m−3 and 4.0 ± 1.6 μg EC m−3 in CQ, respectively. Organic matter (1.6OC) plus EC contributed about 40% of PM2.5 mass and displayed weak regional uniformity. Relatively high ratios of OC to EC were observed in the region with 4.3 for CD, 4.6 for NJ, and 3.8 for CQ, respectively. OC and EC pollution in the region exhibited interesting season-dependent characteristics with the lowest concentrations and OC/EC ratios in summer, but higher levels in other seasons. Higher OC/EC ratios in spring and autumn resulted from biomass burning, and in winter were from the enhanced secondary organic aerosol formation under favorable conditions. The exceptionally high OC and EC levels in May and October, mostly notable in CD, resulted from the burning of agricultural residues during harvest period. The high K+concentrations and the high Kexcess/EC ratios implied the persistent influence of biomass burning throughout the year. Using a novel technique combing the EC tracer method and potassium mass balance in the aerosols, a K/EC ratio of 1.22 was used to retrieve the OC from biomass burning and the estimated contributions were 30.8%, 28.3%, and 21.9% in CD, NJ, and CQ, respectively, while secondary OC contributions to OC were 26.7%, 24.6%, and 25.7% in CD, NJ, and CQ, respectively.

To evaluate the variations in temporal and spatial distribution of biogenic volatile organic compound (BVOC) emissions in China, historical BVOC emission inventories at a spatial resolution of 36 km × 36 km for the period of 1981–2003 were developed firstly. Based on the time-varying statistical data and Vegetation Atlas of China (1:1,000,000), emissions of isoprene, 37 monoterpenes, 32 sesquiterpenes, and other volatile organic compounds (OVOCs) were estimated using MEGANv2.1 driven by WRF model. Results show China's BVOC emissions had increased by 28.01% at an annual average rate of 1.27% from 37.89 Tg in 1981 to 48.50 Tg in 2003. Emissions of isoprene, monoterpenes, sesquiterpenes, and OVOCs had increased by 41.60%, 34.78%, 41.05%, and 4.89%, respectively. With fixed meteorological variables, the estimated BVOC emissions would increase by 19.25%, resulting from the increasing of vegetation biomass during the last 23 years. On average, isoprene, monoterpenes, sesquiterpenes, and OVOCs were responsible for 52.40%, 12.73%, 2.58%, and 32.29% of the national BVOC emissions, respectively. β-pinene and α-pinene, farnesene and caryophyllene were the largest contributors to the total monoterpene and sesquiterpene emissions, respectively. The highest emissions were found over northeastern, southeastern, southwestern China, Qinling Mountain, and Hainan and Taiwan provinces. The regions with high emissions had been expanding over the years, especially in the Changbai Mountain, southern China, and southwestern forest regions. The lowest emissions in southern China occurred in 1984–1988. Almost all the provinces had experienced increasing emissions, but their contributions to the national emissions differed significantly over the past 23 years. Yunnan, Guangxi, Heilongjiang, Jiangxi, Fujian, Guangdong, and Sichuan provinces always dominated the national BVOC emissions, excluding in 1977–1981, when the three northeastern provinces had relatively lower emissions.

Ambient volatile organic compounds (VOCs) were measured intensively using an online gas chromatography–mass spectrometry/flame ionization detector (GC–MS/FID) at Ziyang in the Chengdu–Chongqing Region (CCR) from 6 December 2012 to 4 January 2013. Alkanes contributed the most (59%) to mixing ratios of measured non-methane hydrocarbons (NMHCs), while aromatics contributed the least (7%). Methanol was the most abundant oxygenated VOC (OVOC), contributing 42% to the total amount of OVOCs. Significantly elevated VOC levels occurred during three pollution events, but the chemical composition of VOCs did not differ between polluted and clean days. The OH loss rates of VOCs were calculated to estimate their chemical reactivity. Alkenes played a predominant role in VOC reactivity, among which ethylene and propene were the largest contributors; the contributions of formaldehyde and acetaldehyde were also considerable. Biomass burning had a significant influence on ambient VOCs during our study. We chose acetonitrile as a tracer and used enhancement ratio to estimate the contribution of biomass burning to ambient VOCs. Biomass burning contributed 9.4%–36.8% to the mixing ratios of selected VOC species, and contributed most (>30% each) to aromatics, formaldehyde, and acetaldehyde.

Presence of atmospheric PAHs in urban and suburban region (Beijing, China) was studied in April and July 2011. Forty-four pairs of gas and particle (TSP) phase samples were collected every six day by high volume (Hi-Vol) air samplers at four sampling sites, and determined separately by GC/MS based on USEPA Method TO-13A. Average total concentration (gas + particles) of PAHs (T-PAHs) was 135.1±49.0 ng/m3 and 181.2±40.9 ng/m3 in April and July, respectively. Gas phase PAHs (G-PAHs) was the major fraction, comprising 63–92% of T-PAHs. Lighter (2-, 3-, 4-ring) and heavier (5-,6-ring) PAHs were found predominantly in gas and particle phase, respectively. 2- to 6- ring PAHs contributed 10%, 53%, 26%, 7% and 4% of T-PAHs, respectively. Five major PAHs, naphthalene (NAP), fluorene (FLU), PHE, fluoranthene (FLA), and pyrene (PYR) contributed 70 – 90% of T-PAHs. G-PAHs increased significantly while PAHs in particle phase (P-PAHs) decreased from April to July. Volatilization from soil and more emission from power generation increase might explain the increase of G-PAHs, and the washout of P-PAHs along with particles might explain the decrease of P-PAHs. Given particulate organic carbon (OC) and elemental carbon (EC) being well correlated, P-PAHs was moderately correlated with OC and EC, suggesting that there were other mechanisms contributing to P-PAHs different from those of OC/EC. Significant correlation between P-PAHs with SO2 and NO2 suggested coal combustion and automobile exhaust to be contamination contributors.

Based on the official statistics, locally measured emission factors, and the vehicular emission factor model most suitable for China, we developed a black carbon (BC) emission inventory for 2008 in China and at a spatial resolution of 0.5°×0.5°. In 2008, the total BC emissions in China were 1604.94 Gg. Industry and the residential sector were the dominant contributors, estimated at 695.03 Gg and 636.02 Gg of BC, respectively. Together, these two source types contributed 82.9% of the total emissions. Emissions from transportation were 194.63 Gg, accounting for 12.1% of the total. Since emission contributions from different sectors showed significant spatial diversity among the 31 administrative districts, we divided the districts into four categories: industry contribution district, residential contribution district, industry and residential contribution district, and transportation contribution district. As for energy consumption, coal and biofuel contributed 51.0% and 32.2%, respectively, of the total emissions. Spatially, BC emissions in China were unevenly distributed, higher in the east and lower in the west, corresponding to regional economic development and rural population density. High emission districts, covering 5.7% of the territory, contributed 41.2% of the total emissions. Shanxi, Hebei, Shandong, Henan, and Sichuan were the largest contributors to national BC emissions.

The vehicular emission trend in China was tracked for the recent period 2006–2009 based on a database of dynamic emission factors of CO, nonmethane volatile organic compounds (NMVOC), NOx, PM10, CO2, CH4, and N2O for all categories of on-road motor vehicles in China, which was developed at the provincial level using the COPERT 4 model, to account for the effects of rapid advances in engine technologies, implementation of improved emission standards, emission deterioration due to mileage, and fuel quality improvement. Results show that growth rates of CO and NMVOC emissions slowed down, but NOx and PM10emissions continued rising rapidly for the period 2006–2009. Moreover, CO2, CH4, and N2O emissions in 2009 almost doubled compared to those in 2005. Characteristics of recent spatial distribution of emissions and emission contributions by vehicle category revealed that priority of vehicular emission control should be put on the eastern and southeastern coastal provinces and northern regions, and passenger cars and motorcycles require stricter control for the reduction of CO and NMVOC emissions, while effective reduction of NOx and PM10 emissions can be achieved by better control of heavy-duty vehicles, buses and coaches, and passenger cars. Explicit provincial-level Monte Carlo uncertainty analysis, which quantified for the first time the Chinese vehicular emission uncertainties associated with both COPERT-derived and domestically measured emission factors by vehicle technology, showed that CO, NMVOC, and NOx emissions for the period 2006–2009 were calculated with the least uncertainty, followed by PM10 and CO2, despite relatively larger uncertainties in N2O and CH4 emissions. The quantified low uncertainties of emissions revealed a necessity of applying vehicle technology- and vehicle age-specific dynamic emission factors for vehicular emission estimation, and these improved methodologies are applicable for routine update and forecast of China's on-road motor vehicle emissions.

Aiming to reduce the large uncertainties of biogenic volatile organic compounds (BVOCs) emissions estimation, the emission inventory of BVOCs in China at a high spatial and temporal resolution of 36 km × 36 km and 1 h was established using MEGANv2.1 with MM5 providing high-resolution meteorological data, based on the most detailed and latest vegetation investigations. BVOC emissions from 82 plant functional types in China were computed firstly. More local species-specific emission rates were developed combining statistical analysis and category classification, and the leaf biomass was estimated based on vegetation volume and production with biomass-apportion models. The total annual BVOC emissions in 2003 were 42.5 Tg, including isoprene 23.4 Tg, monoterpene 5.6 Tg, sesquiterpene 1.0 Tg, and other VOCs (OVOCs) 12.5 Tg. Subtropical and tropical evergreen and deciduous broadleaf shrubs, Quercus, and bamboo contributed more than 45% to the total BVOC emissions. The highest biogenic emissions were found over northeastern, southeastern, and southwestern China. Strong seasonal pattern was observed with the highest BVOC emissions in July and the lowest in January and December, with daily emission peaked at approximately 13:00 or 14:00 local time.

In order to control regional acid deposition pollution, it is necessary to determine scientific regional control targets for atmospheric acid deposition. This study proposed a method to conduct multi-site simulation using the VSD model and the simulation results were plotted by cumulative frequency distribution curves. Then the regional acid deposition control targets were determined based on the analysis of the restoration of the soil in the region under different deposition scenarios in the target years. The method was applied in the Guangzhou-Dongguan-Huizhou region. To analyze the control targets for acid deposition in this region, 25 sites were simulated by VSD model based on onsite soil sampling and investigation, and the results were plotted by cumulative frequency distribution curves. The results indicated that when S deposition was controlled alone and if the protection rate was 80%, the S control targets should be 7.68-12g.m-2.yr-1 in the short-term and 10.24-16g.m-2.yr-1 in the long-term; the short-term and long-term S deposition control targets should be 5.12-8g.m-2 .yr-1 和7.68-12g.m-2 .yr-1 if the protection rate was 95%. When the S and BC depositions were controlled simultaneously and if the protection rate was 80%, the S control targets should be 2.56-4 g.m-2 .yr-1 in the short-term and 5.12-8 g.m-2 .yr-1 in the long-term when BC deposition was 6.4-12.8 g.m-2 .yr -1 ; and the S control targets should be 2.56-4 g.m-2.yr-1 when BC deposition was 4.8-9.6 g.m-2.yr-1. If the protection rate was 95%, the S control targets should be 0.64-1 g.m-2.yr-1 in the short-term and 5.12-8 g.m-2.yr-1 in the long-term when BC deposition was 6.4-12.8 g.m-2.yr-1; and the S control targets should be 0.64-1g.m-2.yr-1 in the short-term and 2.56-4g.m-2.yr-1 in the long-term. When BC deposition was 2-4 g.m-2.yr-1, S deposition should be controlled to 0.64-1g.m-2.yr-1 for the protection rate of 80% and 95%, and some manual restoration measures are required at the same time