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Chemical Composition and Deposition Characteristics of Precipitation into a Typical Temperate Forest in Northeastern China

CAS Key Laboratory of Forest Ecology and Management, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China
University of Chinese Academy of Sciences, Beijing 100049, China
Qingyuan Forest CERN, National Observation and Research Station, Shenyang 110016, China
Key Laboratory of Stable Isotope Techniques and Applications, Shenyang 110016, China
Weifang Academy of Modern Agriculture and Ecological Environment, Weifang 261000, China
Key Laboratory of Geospatial Technology for the Middle and Lower Yellow River Regions, Ministry of Education, College of Geography and Environment Science, Henan University, Kaifeng 475004, China
College of Natural Resources Management & Tourism, Mwalimu Julius K. Nyerere University of Agriculture & Technology, Musoma P.O. Box 976, Tanzania
Author to whom correspondence should be addressed.
Forests 2022, 13(12), 2024;
Submission received: 17 October 2022 / Revised: 26 November 2022 / Accepted: 27 November 2022 / Published: 29 November 2022
(This article belongs to the Special Issue Nitrogen Biogeochemical Cycling in Forest Ecosystems)


The chemical compositions and deposition characteristics of atmospheric precipitation affect the structure and function of forest ecosystems and reflect regional air quality. Although northeastern China constitutes a vital forested area, few relevant studies reveal the chemical composition and the nitrogen (N) and sulphur (S) deposition characteristics within precipitation. In this study, we monitor precipitation chemistry during 2018–2020 at a rural forested site in northeastern China (the Qingyuan site) and compare it with those from background sites (Mondy in Russia and Ochiishi in Japan) and highly anthropogenically influenced areas (Beijing). The precipitation pH range was 4.7–8.0 (volume-weighted average 6.2). The average concentration of total ions in precipitation was 459 μmol L−1, representing a moderate pollution level. Nitrate (NO3, 73 μmol L−1) and ammonium (NH4+, 133 μmol L−1) were the major anions and cations in the precipitation. Total inorganic nitrogen (TIN) deposition was 12.3–15.9 kg N ha−1 year−1 (NH4+-N deposition accounted for 54–67%), lower than the average level in China (19.4 kg N ha−1 year−1). Annual precipitation sulphate (SO42−) deposition was 4.9–6.7 kg S ha−1 year−1. Seventy-two percent of the precipitation ions at our site originated from human activities. This work has revealed that N and S deposition is an important ion deposition component in atmospheric precipitation in the study of temperate forests in northeastern China. Nitrogen deposition, as a source of vital nutrients in the forest ecosystem, may promote forest growth and, thereby, forest carbon sequestration.

1. Introduction

Atmospheric concentrations of nitrogen oxides (NOx), ammonia (NH3) and ammonium (NH4+) (collectively NHx), and sulphur dioxide (SO2), and the deposition of nitrogen (N) and sulphur (S) have rapidly increased over the past 100 years owing to increasing anthropogenic emissions, such as massive fossil fuel combustion, motor vehicle emissions, and fertiliser application [1,2,3,4]. Precipitation is a necessary removal process for atmospheric pollutants; therefore, the chemical composition of atmospheric precipitation can reflect regional atmospheric pollution status. In addition, precipitation is a water source for forest ecosystems and a significant nutrient source, such as of N, S, Ca, and Mg [5]. Through precipitation, rain (snow) water carries airborne aerosol particles and gases, such as NOx, NHx, and SO2, into forest ecosystems [6,7]. Although elevated N deposition input can enhance plant productivity and carbon sinks in forest ecosystems, excess N loading can have many negative effects, including soil acidification, eutrophication of water bodies, loss of biodiversity, and forest decline [3,8,9,10,11,12]. By analysing the ion deposition level in atmospheric precipitation, it is possible to effectively infer the input of nutrients into forest ecosystems [13] and determine the extent of atmospheric pollution in the region and the pathways of material sources through changes in chemical components, pH, and other chemical characteristics [14]. Therefore, the chemical composition and deposition of precipitation in forests have been significant issues in forest hydrological and ecological research [5,9,11,15].
China is one of the regions worldwide with high nitrogen and sulphur deposition [16,17]. Atmospheric N deposition in China increased by 60% from 1980 to 2010 [18] and stabilised from 2016 to 2020 [19] because of the socioeconomic development and transformation of the energy structure. The total nitrogen deposition in northern China is 60.6 kg N ha−1 year−1 [20], which is much higher than the monitored results in North America (5.3 kg N ha−1 year−1), East Asia (8.7 kg N ha−1 year−1), Europe (10.6 kg N ha−1 year−1), and other countries or regions [21,22,23,24,25,26]. An increasing number of studies have shown the current status of precipitation chemistry in urban and suburban areas of China. However, rural and forested areas in China have been paid less attention compared to urban or suburban areas, possibly because of the relatively less severe pollution, public awareness, and the attention paid to environmental issues from the big cities [27,28,29,30,31]. Forested areas are more sensitive to air pollution and can show a larger response to N deposition than their non-forested counterparts. Related deposition characteristics of certain ions in forested areas, i.e., nitrogen and sulphur, were even less demonstrated.
The temperate forest region in northeastern China is located in the Daxing’anling, Xiaoxing’anling, and Changbai Mountains, the largest natural forest area in China and a natural ecological barrier for the northeastern, northern China, and even northeastern Asia. With high forest cover, it represents a crucial carbon sink and plays an essential role in absorbing CO2 and mitigating climate warming. Precipitation carrying various ions serves as a source of nutrient input into forest ecosystems. By alleviating N limitation in some forests, N deposition would lead to enhanced plant productivity and potentially forest C sink. Several studies indicated that large long-term N deposition resulted in the N saturation phenomenon in Japan and China. After that, forest decline symptoms were observed in Japan and China [32]. However, information about air pollution in China’s rural forests is scarce. Few rural forest lands are subjected to ambient air quality measurements in northeastern China [33], with few studies reporting precipitation deposition of ions in forested regions therein [32,33,34,35]. We particularly lack data on the amounts and sources of atmospheric deposition into rural forests in northeastern China. Therefore, quantifying and assessing ion deposition in temperate forests in northeastern China, and comparing it with heavily polluted and clean areas, is essential.
In this study, we collected precipitation samples on a daily basis at a rural Qingyuan forest site in northeastern China from 2018 to 2020 to analyse the chemical composition of the precipitation. We compared this site with a high-deposition site in Beijing, representative of northern China, and two remote monitoring sites in East Asia (Mondy and Ochiishi) as low-deposition background sites. The objective of our research was to: (1) analyse the chemical composition characteristics of precipitation from the study forest and quantify the deposition of major ions in typical temperate forest areas in northeastern China; (2) assess the current status through comparison with other high and background sites; and (3) determine the contribution of anthropogenic sources and related transport pathways of major precipitation ions and provide a reference basis for atmospheric environmental management. We hypothesized that N and S deposition into our study forest would be lower than those of urban areas in China and still much higher than those into very remote sites at similar latitudes.

2. Materials and Methods

2.1. Study Site and Sample Collection

Precipitation samples were collected from the Qingyuan Forest Station of the Chinese Ecosystem Research Network (Qingyuan Forest CERN), located in Qingyuan County, Liaoning Province, northeastern China (41°51′ N, 124°54′ E), at altitudes of 578 m. The area has a continental monsoon climate with warm and rainy summers and cold and dry winters [36]. Over the 3-year study period, the annual mean air temperature was 4.8 °C, with a minimum of −27.8 °C in January and a maximum of 27.0 °C in July. The mean annual precipitation was 743 mm, with the rainy season concentrated in summer and autumn, accounting for 70–80% of the yearly rainfall. There are no prominent pollution sources within a 50 km radius of the study area, and this region is with forest coverage of over 90% [36,37].
A total of 109 rain and 13 snow samples were collected from January 2018 to December 2020. Detailed information on the sample collection and processing procedures is available from Huang et al. (2019) [38]. The bulk deposition samples were collected on a daily basis with three manual rain gauges (20 cm in diameter). The samplers were installed at 1.5 m aboveground at the meteorological observation field (25 m × 25 m) [37,38,39], with no trees or high buildings nearby. After each collection, the samplers were washed with deionized water (Figure 1, Figure S1). The pH of the precipitation samples was measured immediately after collection. Precipitation samples were filtered through 0.45 μm nylon membrane filters and stored in clean 100 mL white polypropylene plastic bottles at −20 °C until chemical analysis. Snow samples were melted at room temperature, filtered, and stored according to the precipitation procedure. Three precipitation samples collected from three separate samplers during the same period were mixed as one composite sample to determine ion concentrations. The monitoring included precipitation amount, pH, NH4+, Ca2+, K+, Mg2+, Na+, SO42−, NO3, Cl, and F concentrations in precipitation.

2.2. Chemical Analyses

Chemical analyses were conducted at the Stable Isotope Laboratory of the Institute of Applied Ecology, Chinese Academy of Sciences. Concentrations of major inorganic ions in the samples were determined using an ion chromatograph (DionexTM, ICS-600, Thermo Fisher Scientific Instrument Co., LTD, USA) with an instrument accuracy of 5%. The concentrations of cations (NH4+, Ca2+, K+, Mg2+, and Na+) were determined using DionexTM CS12A and CQ12A columns, with 10 mmol L−1 methanesulfonic acid as the eluent at a flow rate of 1.0 mL min−1. The concentrations of anions (SO42−, NO3, Cl, and F) were determined using the DionexTM AS22 and AQ22 columns. The leaching solution was 4.5 mmol L−1 of sodium carbonate (Na2CO3) plus 1.4 mmol L−1 sodium bicarbonate (NaHCO3) with a flow rate of 1.2 mL min−1.

2.3. Data Sources and Quality Control

The total anion and cation concentrations of the precipitation samples were determined to control their quality standards by referring to the United States Environmental Protection Agency (USEPA) [40] and the Technical Manual for Wet Deposition Monitoring in East Asia, 2010 [41]. Additional ionic imbalances may be due to unmeasured organic acids in rainwater [41], such as hydrogen carbonate (HCO3) and organic ions like formate (HCOO) and acetate (CH3COO), which were not measured, and data regarding any organic acids were not available. After rejecting the data that did not meet the quality criteria, linear regression analysis was performed between the grand total of anion concentration and the grand total of cation concentration for the Qingyuan site. The regression analysis of total anion and cation concentrations was performed for all valid precipitation samples over three years, and the R2 of linear regression was 0.75 with a slope of 1.16 (Figure S2). This indicated that the correlation between the total anions and cations was high, and all samples met the requirements.
We selected two clean sites in East Asia with similar latitudes to the Qingyuan site as background sites to compare. One site was Ochiishi (43°09′ N, 145°29′ E) in Hokkaido City, Japan, with an elevation of 49 m, representing a clean seaward site. The other site was Mondy (51°37′ N, 100°55′ E) in Russia, with an elevation of 1996 m, representing a clean inland site (Figure 1). Both sites had low anthropogenic disturbances and were located more than 50 km from significant pollution sources, such as cities, power plants, and highways, and more than 500 m from major roads (less than 500 vehicles per day). Data from these two sites were obtained from the Acid Deposition Monitoring Network in East Asia during 2018–2020 (EANET) (, accessed on 20 April 2022). We further selected two other sites in urban (39°57′ N, 116°19′ E) [32] and rural (40°29′ N, 115°59′ E) [31] Beijing (Figure 1) to represent sites highly influenced by urban and agricultural activities to compare with our site (rural).

2.4. Quantitative Assessment of Ion Sources

The principal sources of ion components in rainwater include seawater sputtering, rock (soil) weathering, and anthropogenic activities (excluding the contributions of volcanoes and other natural sources [42]. To further investigate the different sources of ions in the Qingyuan precipitation, Equations (1) to (3) were used to calculate the relative contributions of the sea salt fraction (SSF), crust fraction (CF), and anthropogenic source fraction (AF):
SSF = (X/Na+) seawater/(X/Na+) precipitation × 100%
CF = (X/½Ca2+) soil/(X/½Ca2+) precipitation × 100%
AF = 100% − SSFCF
where X is the target ion (μmol L−1), the ratio of [X/Na+] seawater refers to the existing literature [43], and the [X/Ca2+] soil is from the crustal composition (Table S1) [44]. The contributions of the crustal, oceanic, and anthropogenic sources to the ions were calculated to quantify the origins of the major ions.

2.5. Air Mass Back Trajectories

In this study, the air mass movement was set to end at the Qingyuan site using the downloaded Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) version 4.0 (, accessed on 10 August 2022) [45,46]. The simulation height was 500 m (above ground level) [47], the simulation time was post-72 h, and the sampling period was within 12 h (0:00, 12:00, and 24:00 UTC). The airflow trajectory was clustered according to the velocity and direction of the horizontal airflow movement. The 3-day backward trajectories were calculated for all sampling days to track the source of water vapour in this study area for different seasons during 2020. The meteorological data used in the trajectory model were obtained from the global data assimilation system of the National Centers for Environmental Prediction (NCPE) of the United States.

2.6. Calculations and Statistical Analyses

The annual NO3-N deposition in precipitation is the sum of the NO3 concentration multiplied by the precipitation amount for each precipitation sampling over the same year [29]. Other ion depositions were calculated in the same way as the NO3-N deposition. Significance analysis was performed using the SPSS software (version 26.0; SPSS Inc., Chicago, IL, USA). One-way analysis of variance (ANOVA) was used to examine the seasonal variation of ion concentrations and deposition differences using the Duncan method. LOESS (Local Regression) was performed to fit loess-smoothed curves to determine monthly trends of ion concentration. Pearson’s correlation analysis was used to determine the relationship between monthly precipitation, monthly mean ion concentration, and monthly deposition, and the analyses were performed using the R statistical software 4.1.2. Statistical significance was set at p < 0.05 unless otherwise stated.

3. Results

3.1. Ion Concentration and Deposition in Precipitation

During the study period, the pH of precipitation at the Qingyuan site ranged from 4.7 to 8.0 (average 6.2, Table 1). The total ion concentration of the precipitation ranged from 421 to 530 μmol L−1 (average 459 μmol L−1). The main cations in the precipitation were NH4+ and Ca2+, accounting for 29% and 19% of the total precipitation ion concentration, respectively (Figure 2c). The NH4+ and Ca2+ concentrations were 97–175 (133 ± 40) μmol L−1 and 74–101 (87 ± 13) μmol L−1, respectively. The dominant anions were NO3 and SO42−, accounting for 16% and 14% of the total ion concentration, respectively (Figure 2c). The NO3 and SO42− concentrations were 58–85 (73 ± 14) μmol L−1 and 56–72 (61 ± 9) μmol L−1, respectively.
Over the study period, the NO3-N and NH4+-N deposition rates were 4.9–7.3 (5.9 ± 1.3) kg ha−1 year−1 and 6.7–19.8 (8.4 ± 1.6) kg ha−1 year−1, respectively. SO42−-S deposition ranged from 4.9 to 6.7 (5.6 ± 0.9) kg S ha−1 year−1 (Table 1), and Ca2+ and Mg2+ depositions were 6.6–12.1 (8.9 ± 2.9) kg Ca ha−1 year−1 and 0.9–2.2 (1.7 ± 0.7) kg Mg ha−1 year−1, respectively (Table 1).

3.2. Seasonal Variations and Controlling Factors of Ion Concentration and Deposition

The average annual precipitation over the three-year study period was 680 mm. Precipitation was concentrated in summer (June to August) and autumn (September to November), accounting for 71% of the total annual precipitation. Seasonal trends in ion concentrations were observed, being markedly higher in spring (March to May) and winter (December to February) than in summer and autumn (Figure 3, Table 2). The average NO3 concentrations were 125 ± 40, 68 ± 37, 61 ± 20, and 111 ± 66 μmol L−1 in spring, summer, autumn, and winter, respectively (Figure 3b, Table 2). While the corresponding average NH4+ concentrations were 123 ± 96, 120 ± 57, 79 ± 70, and 206 ± 127 μmol L, respectively (Figure 3c, Table 2). The SO42− concentrations averaged 181 ± 95, 61 ± 40, 74 ± 27, and 148 ± 102 μmol L−1 in the spring, summer, autumn, and winter, respectively (Figure 3b, Table 2). The seasonal variation in Ca2+ and Mg2+ concentrations were similar. In contrast, the seasonal characteristics of ion deposition were higher in spring and summer and lower in autumn and winter. Nitrate nitrogen deposition averaged 14 ± 7, 13 ± 7, 7 ± 1, and 3 ± 4 mol ha−1 month−1 in spring, summer, autumn, and winter, respectively, while NH4+-N deposition averaged 105 ± 59, 67 ± 23, 36 ± 30, and 21 ± 20 mol ha−1 month−1 (Table 2). The precipitation of SO42−-S deposition was 41 ± 25, 25 ± 12, 16 ± 6, and 5 ± 4 mol ha−1 month−1 in spring, summer, autumn, and winter (Table 2). The seasonal trends of Ca2+ and Mg2+ depositions were also similar.
We observed a seasonal pattern of significantly higher ion deposition in spring and summer than in autumn and winter. In addition, nitrate deposition was significantly and positively correlated with precipitation amount and concentration (r = 0.43, p < 0.05; Figure 4a,c), indicating that NO3 deposition was related to the amount of precipitation and atmospheric NO3 concentration. Except for NO3, other ion depositions (including NH4+, SO42−, Ca2+, and Mg2+) in precipitation showed a significant positive correlation with concentration (Figure 4c,d) and no significant correlation with precipitation amount (Figure 4a,b), indicating that the ion concentration is the main factor affecting the magnitude of deposition.

3.3. Sources of Ions in Precipitation

The source contribution calculation results are shown in Figure 5. Anthropogenic input contributed the most to NH4+, NO3, SO42−, and K+ in precipitation, with contributions all above 90% (100%, 100%, 90%, and 96%, respectively; Figure 5). Generally, K+ is mainly a land-phase input owing to the weathering of potassium feldspar and potassium-containing minerals that are eventually stored in precipitation. For K+ in this study, the contribution of anthropogenic activities was as high as 96%, possibly owing to various anthropogenic activities, such as road dust, construction of buildings, biomass burning, and the use of potassium fertiliser. Land-phase input contributed the most to Ca2+ and Mg2+ in precipitation, accounting for 97% and 41%, respectively. These are characteristic ions that respond to terrestrial sources. The marine-phase input contributed the most to Cl in precipitation (84%), indicating that Cl is mainly a sea-saline ion. Simultaneously, the Cl in precipitation was influenced by anthropogenic activities (16%). Human sources of Cl in precipitation may include industrial emissions and coal-combustion heating in the winter of the study regions. As a whole, atmospheric precipitation ions were mainly derived from anthropogenic activities (72%) at the Qingyuan site, followed by land-phase (19%) and marine (9%) sources, indicating that the atmospheric ecology of the site is influenced mainly by anthropogenic activities (Figure S3).
The HYSPLIT 4.0 shows that the atmospheric precipitation water vapour sources at the Qingyuan site had significant seasonal variations during the study period. The source of ions in precipitation was influenced by water vapour transported from distant areas (24–44%), in addition to the local circulating water vapour clouds (Figure S4). Spring and summer water vapours are influenced by the East Asian monsoon, with sources mainly from the western Pacific (27–31%; Figure S4a,b). Autumn and winter water vapour were primarily derived from westerly transport with the influence of polar air masses in the Mongolian direction (32–44%; Figure S4c,d). Water vapour transport from the persistent southeast and northwest air currents at low altitudes, which also carries acidic pollutants, affects the air quality at the Qingyuan site, thus changing the precipitation chemistry characteristics in this study area, further confirming our observations.

4. Discussion

4.1. Comparisons with Urban and Background Regions

Precipitation is an effective scavenging pathway for removing gases and aerosol pollutants from the atmosphere [6,9,48]. Therefore, the chemical composition of precipitation reflects the level of atmospheric pollution. To better evaluate the current atmospheric pollution level at the Qingyuan site, this study site was compared with the Beijing urban (2018–2020) and rural (2017–2018) sites and the two background sites of Ochiishi and Mondy (2018–2020). The calculation results of the source contributions showed that the precipitation ion concentration at the Qingyuan site was most influenced by anthropogenic sources (72%). In comparison, the land-phase and marine sources accounted for 19% and 9%, respectively. The contribution of each source in Qingyuan was similar to that in Beijing (Figure S3). However, the contribution of anthropogenic sources to precipitation ions at the Qingyuan site was higher than that at the clean inland Mondy site (41%) and approximately three times that at the clean coastal Ochiishi site (26%). These results suggest that the rapid development of the regional economy and urbanisation have influenced the chemical composition of precipitation at the Qingyuan site.
We further assessed the type and level of precipitation pollutants at the Qingyuan site based on the total ion concentration. The annual mean total ion concentration of precipitation during the study period was 459 μmol L−1. For total ion concentration of precipitation, our site was 25% lower than the Beijing urban site (average 575 μmol L−1) but two times higher than the coastal background site Ochiishi (average 230 μmol L−1) and 15 times higher than the clean inland background site Mondy (average 29 μmol L−1; Figure 2). Comparing annual ion deposition of precipitation (including NO3-N, NH4+-N, SO42−-S, Ca2+, Mg2+, Cl, Na+, and K+), the 44 kg ha−1 year−1 in the Qingyuan site amounted 64% of that in Beijing urban site (68 kg ha−1 year−1), 80% of that in Ochiishi (55 kg ha−1 year−1, mainly contributed by Cl and Na+), and about 30 times that in Mondy (1.5 kg ha−1 year−1; Table 1). This indicates that the northeast temperate forest region has cleaner precipitation than the urban region. However, it still experiences moderate to high pollution compared to the Mondy and Ochiishi regions.
The chemical composition of the precipitation at our study site was also markedly different from those at the other sites. The inorganic ions of atmospheric precipitation at the Qingyuan site consisted mainly of NH4+, Ca2+, NO3, and SO42−. The sum of their molar concentrations accounted for 78% of the total ion concentrations in precipitation (Figure 2c), similar to the values reported for many urban areas, such as Beijing (89%), Shanghai (81%), Kathmandu (88%) in Nepal, and Klang Valley (86%) in Malaysia [29,49,50,51,52]. However, the molar concentration at Qingyuan was 9% lower than that in the urban area of Beijing (87%). It was 20% higher than that in the rural area of Beijing (58%), and 5.6 and 2.6 times higher than those in Ochiishi (14%) and Mondy (30%), respectively. The clean ion of Cl and Na+ concentrations in the Qingyuan site accounted for 12% of the total ion concentration in precipitation (Figure 2c), which was much lower than the two background sites, Ochiishi (82%; Figure 2d) and Mondy (64%; Figure 2e), and other coastal cities, such as Satkhira (41%) in Bangladesh and Falta (55%) in India [51].
NH4+-N and NO3-N deposition at our study site accounted for the largest proportion of the total ion deposition (24–44%). The average annual inorganic nitrogen deposition (TIN, NO3-N plus NH4+-N) at this study site was 14.3 kg N ha−1 year−1 (Table 1), which is 29% lower than the Chinese average (19.4 ± 0.8 kg N ha−1 year−1, based on monitoring from 2011 to 2018) [53], 35% lower than the Beijing urban area (22 kg N ha−1 year−1), and 13% lower than the Beijing rural area (16.4 kg N ha−1 year−1). However, the deposition at the Qingyuan site was 1.5 times the multiannual wet deposition fluxes from 27 sites across Europe in 2000–2017 (4.0–9.8 kg N ha−1 year−1) [24], higher than that at eight Japanese sites (3.0–12.9 kg N ha−1 year−1) [54], seven times higher than that in Mondy (0.4 kg N ha−1 year−1), and higher than that in Ochiishi (2 kg N ha−1 year−1) by 35 times. Thus, the N deposition level in the Qingyuan site is moderate to high. Furthermore, since our sampling gauges are open all the time, we collected all wet deposition plus part of the dry deposition, i.e., bulk deposition. Dry deposition can account for half to two-thirds of the total nitrogen deposition in many cases [5,55]. Thus, our measured bulk deposition could be lower than the total N deposition at the study forest (wet + dry deposition). Dry deposition fluxes (1.1 to 52.2 kg N ha−1 year−1) of Nr species on average contributed 52% to the total N deposition (2.9 to 83.3 kg N ha−1 year−1) across the 43 monitoring sites in China, indicating the importance of dry deposition monitoring for a complete N deposition assessment at the national scale. Future studies are needed to measure both wet and dry deposition separately to better depict the impacts of N deposition forest ecosystems in northeastern China.

4.2. Seasonal Variations, Sources, and Controlling Factors of Ion Concentration & Deposition

The NO3, SO42−, NH4+, and Mg2+ concentrations at the Qingyuan site showed significant seasonal variation (Figure 3, Table 2). The NH4+ concentrations in precipitation were slightly higher in winter than in spring and summer, significantly higher than in autumn, and approximately 2.6 times higher than in autumn (p < 0.05, Table 2). The high concentrations in winter are may results from the increase in the combustion of fossil fuels (coal-based) and biomass fuels (straw- and fuelwood-based) [56], an increase in motor vehicle emissions [57,58], and the low temperature and precipitation in winter and stable meteorological conditions that slow the diffusion and dilution of NH3. The higher NH4+ concentrations in spring and summer may be due to increased NH3 volatilisation from livestock manure decomposition and fertiliser application caused by high temperatures, being consistent with findings reported by Chang et al. (2016), Gu et al. (2015), and David et al. (2014) [58,59,60]. In addition, higher precipitation in autumn and the dilution effect of precipitation on NH3 and NH4+ in the atmosphere are the reasons for the low NH4+ concentration in precipitation in autumn.
Main sources of atmospheric NOx include lightning, coal and biomass combustion, motor vehicle exhaust emissions, and soil biological processes (nitrification and denitrification) [61,62,63]. The concentrations of NO3 in atmospheric precipitation at our study site in winter and spring were 1.6–2.0 times higher than in summer and autumn (p < 0.05, Table 2), consistent with reported values in Beijing and Chizhou by Xu et al. (2015) [63]. The higher NO3 concentrations in winter and spring were likely due to coal and biomass combustion and NOy (NOx plus HNO3 plus NO3) emissions from motor vehicle exhaust [64]. Wen et al. (2020) reported that the cumulative NOx emissions from biomass combustion were 1.7 × 104 Gg [31]. According to the Ministry of Ecology and Environment of China, in 2020, NOy emissions from motor vehicles accounted for 40% of total pollutant emissions nationwide. In addition, the variation in NO3 concentration depended strongly on temperature (r = −0.39, p < 0.01, Figure S5). Despite its lower temperature, winter favours the presence of NO3 in the particulate matter. High temperatures and precipitation in summer and autumn are not favourable for NO3 to exist as particulate matter. Therefore, NO3 concentrations are lowest in summer and autumn [65].
There was no significant seasonal variation in NH4+-N and NO3-N deposition at the Qingyuan site (p > 0.05); however, TIN deposition was significantly greater in spring and summer than in winter (data not shown). The TIN deposition was 5.7 times higher in spring and summer (1.7 and 2.0 kg N ha−1 year−1, respectively) than in winter (0.4 kg N ha−1 year−1). Precipitation was also 4.3 and 5.0 times higher in spring and summer (49 and 105 mm, respectively) than in winter (26 mm). Therefore, we suggest that increased precipitation was one of the main reasons for the higher TIN deposition in spring and summer, similar to that observed by Fang et al. (2013) in Chengyang District, Qingdao City [30].
Emissions from agricultural sources mainly influence TIN deposition during spring and summer when agricultural activities are intensified. Fertiliser application and volatilisation of livestock manure in spring and soil biological processes (nitrification and denitrification) lead to increased atmospheric NHx and NOx emissions. Gu et al. (2015) obtained results similar to our site [59]. Lower winter temperatures and no fertilisation activities influence TIN deposition by coal, biomass burning, and motor vehicle exhaust emissions [61,62]. It is anticipated that N deposition might increase further with the development of the surrounding society and the acceleration of urbanization. However, the current level of N deposition in the Qingyuan forest ecosystems was found to be less serious than projected from urban/suburban data. The control of energy consumption should be strengthened in future to prevent an increase in N deposition in Chinese forest ecosystems.
There was a significant positive correlation between SO42− and NO3 concentrations in precipitation at the Qingyuan site during the study period (r = 0.72, p < 0.01; Figure S5). Both anions showed similar seasonal variations as those reported by Yu et al. (2019) [65] and Zhang et al. (2021) [66]. The SO42− concentration in the precipitation was 2–3 times higher in spring and winter than in summer and autumn (p < 0.05, Table 2). Sulphate in precipitation originates mainly from particulate matter emitted from coal combustion, motor vehicle exhaust, and the gas–liquid phase transformation of atmospheric SO2 [67]. Coal-fired SO2 emissions currently account for more than 90% of the total SO2 emissions in China [68]. We also attribute the rise in SO42− concentrations in precipitation in early spring and winter in northeastern China to increased coal combustion. In addition, emissions from motor vehicles contribute more than other sources in winter [21,58], resulting in SO2 and NOx forming heavier soot and traffic compound pollution. The SO42− concentration in precipitation in summer and autumn was lower owing to the decrease in coal combustion. Temperature was also a factor affecting the atmospheric SO42− concentration (r = −0.25, p < 0.01, Figure S5). High temperatures and strong oxidising substances, such as O3, H2O2, and ·OH, favour the production of SO42− in summer and autumn [68]. However, the amount of coal burned in summer and autumn is much smaller than in winter, resulting in lower atmospheric SO2 levels. Since 71% of the total annual precipitation occurred during summer and autumn, the higher the precipitation, the more significant the SO2 and sulphate removal, leading to a high SO42− concentration in precipitation in summer and autumn [68].
Sulphate deposition in precipitation was significantly greater in spring than in winter, with no significant seasonal differences between summer and autumn (p < 0.05, Table 2). Sulphate deposition was significantly and positively correlated with precipitation and SO42− concentration (R = 0.43, p < 0.05; Figure 4a,c), indicating that SO42−-S deposition was seasonally synchronised with SO42− concentration and precipitation. Although the SO42− concentrations in precipitation were similar in winter and spring, the higher temperatures and more significant precipitation in spring than in winter were more favourable for SO42− production. Therefore, SO42−-S deposition in spring was significantly larger than in winter.
Calcium and Mg are characteristic elements of soil, cement, and lime. Calcium and Mg2+ mainly originate from construction, road dust, soil, and atmospheric entry through rainfall. The seasonal variation in Ca2+ concentration in precipitation at the Qingyuan site was not significant. Magnesium concentrations were greater in spring and winter (dry season) than in summer and autumn (p < 0.05, Table 2) because of low precipitation in spring, increased dust from soil tillage [63], and high wind carrying sand containing many minerals, including Ca2+ and Mg2+ [69]. In contrast, atmospheric Ca2+ and Mg2+ are more likely to be diluted and washed away by heavy rainfall in summer and autumn. Calcium and Mg2+ deposition were significantly higher in spring than in winter, approximately nineteen times and seven times as much as in winter, respectively(p < 0.05, Table 2). This is mainly because of the long-range transport of farmland cultivation and dust in spring and the easy freezing of the ground surface or snow and ice covering in winter, reducing the dust and long-range transport.

5. Conclusions

The present study analysed the chemical composition of precipitation and quantified the major ion deposition to a typical temperate forest in northeastern China from 2018 to 2020. With the social development and industrial structure changes in and around the Qingyuan site, the ions in the precipitation at our study site are becoming more complex, with a 72% contribution from anthropogenic sources. The total inorganic N (TIN) deposition in precipitation ranged from 12.3 to 15.9 kg N ha−1 year−1, accounting for 24–44% of the total annual ion deposition (44 kg N ha−1 year−1). This indicates that N deposition in forest ecosystems in northeastern China is currently at a medium to high level. To mitigate the increase in N deposition, joint prevention and control of air pollution at the Qingyuan station and the surrounding areas should be strengthened. Agricultural sources (fertiliser application and livestock manure), non-agricultural sources (motor vehicle emissions, fossil fuel combustion, and biomass burning), and the transport of distant water vapour clouds led to seasonal variations in ion concentration and deposition at the study site. This study only analysed the three primary sources of atmospheric precipitation ions. We suggest future studies to use the stable isotope natural abundance technique to analyse more specific sources of ions in the precipitation in the study region.

Supplementary Materials

The following supporting information can be downloaded at Figure S1: Bulk deposition samplers in Qingyuan rural sites; Figure S2: The correlation between total anion and cation concentrations in precipitation in the Qingyuan forests over the study period from 2018 to 2020; Figure S3: Contribution of each source to total ions in precipitation in the Qingyuan forest over the study period, compared to Beijing, Mondy, and Ochiishi.; Figure S4: 500 m above ground level air mass backward trajectory analysis for the year 2020 from the NOAA HYSPLIT model. The blue circle indicates our study site (the Qingyuan station). The red star indicates Beijing. The grey lines represent the 72 h backward trajectories of all air masses in each season (spring, summer, autumn, winter) that end in a trajectory at this study site. Spring from March to May, summer from June to August, autumn from September to November and winter from December to February. The red, blue, and green lines show the sources of water vapour in different directions; Figure S5: Correlation of concentrations of major ions in precipitation at the Qingyuan station: blue indicates a positive correlation, red indicates a negative correlation, and the number is the correlation coefficient in the ellipse. ** indicates a significant correlation at the 0.05 level (two-sided), *** indicates a significant correlation at the 0.01 level (two-sided). Table S1: To calculate the different sources of ions in Qingyuan precipitation, the reference values for the ratio of [X/Na+] seawater and [X/Ca2+] soil in Equations (3) to (5) were used to calculate the relative contributions of the sea salt fraction (SSF), crust fraction (CF), and anthropogenic source fraction (AF).

Author Contributions

Conceptualisation, Y.W. and Y.F.; methodology, Y.W. and Y.F.; software, Y.W.; validation, Y.W. and Y.F. formal analysis, Y.W.; investigation, X.F., D.H., and K.H.; data curation, Y.W.; writing—original draft preparation, Y.W.; writing—review and editing, Y.W., Y.F., F.Z., R.K., L.S., S.H., A.S.M. and G.A.G.; visualisation, Y.W. and Y.F.; supervision, Y.F.; funding acquisition, Y.F. and F.Z. All authors have read and agreed to the published version of the manuscript.


This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (grant number XDA28020302, XDA23070103), the Liaoning Vitalization Talents Program (XLYC1902016), the National Natural Science Foundation of China (31770498, 32101291, 31901134), and the Science and Technology Program of Shenyang (No. 21-108-9-06, 22-315-6-18).

Data Availability Statement

The data are included in the article.


We greatly thank Jin Li, Jianfei Wang, Dong Liu, Deze Liu, and Nanxue Liu for their assistance in laboratory work, as well as Yue Liu for R-code suggestions. We are grateful to Qingyuan Forest CERN, the Chinese Academy of Sciences for providing the experimental sites and relevant support.

Conflicts of Interest

The authors declare no conflict of interest.


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Figure 1. The locations of the Qingyuan forest study site and other sites with similar latitudes for comparison. Mondy in Russia and Ochiishi in Japan represent the clean interior and coastal sites, respectively. Beijing-urban and Beijing-rural in China represent the sites heavily influenced by urban and agricultural activities, respectively. The green areas indicate forest cover (URL:, accessed on 18 October 2022).
Figure 1. The locations of the Qingyuan forest study site and other sites with similar latitudes for comparison. Mondy in Russia and Ochiishi in Japan represent the clean interior and coastal sites, respectively. Beijing-urban and Beijing-rural in China represent the sites heavily influenced by urban and agricultural activities, respectively. The green areas indicate forest cover (URL:, accessed on 18 October 2022).
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Figure 2. Percentages (%) of each ion to total ions in precipitation in Beijing urban site (a), Beijing rural site (b), the Qingyuan forest (c), Ochiishi (d), and Mondy (e) for comparison.
Figure 2. Percentages (%) of each ion to total ions in precipitation in Beijing urban site (a), Beijing rural site (b), the Qingyuan forest (c), Ochiishi (d), and Mondy (e) for comparison.
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Figure 3. Monthly mean temperature and monthly mean precipitation amount (a), monthly mean concentrations (b,c), and monthly deposition rates (d,e) of major ions in precipitations in the Qingyuan forest over the period of 2018–2020. Each line in figures (be) indicates the loess-smoothed curve to show the monthly trends of the variables shown by the corresponding colour. The calcium ion concentration values in February 2018, January 2019, and February 2019 are missing because of the lack of enough samples for analysis.
Figure 3. Monthly mean temperature and monthly mean precipitation amount (a), monthly mean concentrations (b,c), and monthly deposition rates (d,e) of major ions in precipitations in the Qingyuan forest over the period of 2018–2020. Each line in figures (be) indicates the loess-smoothed curve to show the monthly trends of the variables shown by the corresponding colour. The calcium ion concentration values in February 2018, January 2019, and February 2019 are missing because of the lack of enough samples for analysis.
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Figure 4. Correlations of monthly deposition of major ions with monthly precipitation amounts (a,b) and their concentrations (c,d) in Qingyuan throughout 2018–2020. Solid lines and dashed lines represent significant and non-significant relations, respectively.
Figure 4. Correlations of monthly deposition of major ions with monthly precipitation amounts (a,b) and their concentrations (c,d) in Qingyuan throughout 2018–2020. Solid lines and dashed lines represent significant and non-significant relations, respectively.
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Figure 5. Contribution of each source to the major ions in precipitation into the Qingyuan forest throughout 2018–2020, compared to other representative sites at comparable latitudes.
Figure 5. Contribution of each source to the major ions in precipitation into the Qingyuan forest throughout 2018–2020, compared to other representative sites at comparable latitudes.
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Table 1. The annual deposition of inorganic NO3-N, NH4+-N, total inorganic N (TIN), SO42−-S, Ca2+, and Mg2+ and the volume-weighted mean pH in precipitation in the Qingyuan forest, compared with another four representative sites with comparable latitudes.
Table 1. The annual deposition of inorganic NO3-N, NH4+-N, total inorganic N (TIN), SO42−-S, Ca2+, and Mg2+ and the volume-weighted mean pH in precipitation in the Qingyuan forest, compared with another four representative sites with comparable latitudes.
kg N ha−1 year−1kg N ha−1 year−1kg N ha−1 year−1kg S ha−1 year−1kg Ca ha−1 year−1kg Mg ha−1 year−1
20–602018–20206.47.315.422.714.720.92.6Sun et al. (2021) [32]
5002017–20186.74.511.916. et al. (2020) [31]
5782018–20206.25.98.414. study
Ochiishi, Japan (Remote)492018–20205. (2020)
Mondy, Russia (Remote)19962018–20205. (2020)
Table 2. Seasonal mean concentrations and deposition of major ions in precipitation at the study site. Numerical means ± SD were given. Superscript letters indicate significant differences among seasons (p < 0.05, one-way ANOVA, Duncan test).
Table 2. Seasonal mean concentrations and deposition of major ions in precipitation at the study site. Numerical means ± SD were given. Superscript letters indicate significant differences among seasons (p < 0.05, one-way ANOVA, Duncan test).
Concentration (μmol L−1)
Spring125 ± 40 a123 ± 96 ab181 ± 95 a324 ± 22568 ± 43 a75 ± 5550 ± 35 a57 ± 38 ab
Summer68 ± 37 b120 ± 57 ab61 ± 40 b82 ± 5715 ± 7 b14 ± 520 ± 9 b20 ± 7 b
Autumn61 ± 20 b79 ± 70 b74 ± 27 b99 ± 7215 ± 7 b58 ± 4614 ± 4 b29 ± 16 b
Winter111 ± 66 a206 ± 127 a148 ± 102 a314 ± 17782 ± 41 a55 ± 4522 ± 21 b72 ± 60 a
Deposition (mol ha−1 month−1)
Spring14 ± 7105 ± 5941 ± 25 a76 ± 60 a17 ± 12 a31 ± 2234 ± 21 a27 ± 22 a
Summer13 ± 767 ± 2325 ± 12 ab32 ± 17 ab6 ± 2 b13 ± 617 ± 8 ab19 ± 10 ab
Autumn7 ± 136 ± 3016 ± 6 ab23 ± 10 ab4 ± 2 b24 ± 228 ± 6 ab13 ± 9 ab
Winter3 ± 421 ± 205 ± 4 b4 ± 3 c3 ± 3 b5 ± 0.31 ± 0.1 c6 ± 0.4 c
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Wang, Y.; Zhu, F.; Kang, R.; Song, L.; Huang, S.; Huang, D.; Huang, K.; Mgelwa, A.S.; Gurmesa, G.A.; Fang, X.; et al. Chemical Composition and Deposition Characteristics of Precipitation into a Typical Temperate Forest in Northeastern China. Forests 2022, 13, 2024.

AMA Style

Wang Y, Zhu F, Kang R, Song L, Huang S, Huang D, Huang K, Mgelwa AS, Gurmesa GA, Fang X, et al. Chemical Composition and Deposition Characteristics of Precipitation into a Typical Temperate Forest in Northeastern China. Forests. 2022; 13(12):2024.

Chicago/Turabian Style

Wang, Yingying, Feifei Zhu, Ronghua Kang, Linlin Song, Shaonan Huang, Dan Huang, Kai Huang, Abubakari Said Mgelwa, Geshere Abdisa Gurmesa, Xiaoming Fang, and et al. 2022. "Chemical Composition and Deposition Characteristics of Precipitation into a Typical Temperate Forest in Northeastern China" Forests 13, no. 12: 2024.

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