上海青浦地区大气降水的化学特征

利用上海青浦地区2003—2014年观测的大气降水监测资料,分析该区域12 a以来大气降水的酸化程度、化学组成特征,探讨降水中化学成分的不同来源及相对贡献。结果表明:降水pH年均值为4.43~6.33,酸雨频率为2.6%~86.8%,降水酸化程度大致经历了明显恶化和波动变化2个阶段。降水电导率年均值为1.77~4.01 m S/m,呈下降趋势。降水中各离子雨量加权平均当量浓度顺序为SO_4~(2-)NH_4~+Ca~(2+)NO_3~-Cl~-Na~+Mg~(2+)F-K~+,SO_4~(2-)、NH+4、Ca~(2+)和NO_3~-是降水中的主要离子,占离子总量的83.0%;降水类型由硫酸型向硫酸和硝酸混合型转变。降水离子中的二次组分SO_4~(2-)、NO_3~-和NH_4~+绝大部分来源于人为源,Ca~(2+)、Mg~(2+)和K+主要来自于土壤源和人为源的贡献,Cl~-主要来自海洋源,同时人为源的影响也不可忽视。     

大连市中心城区地下水“三氮”污染分析

依据《地下水质量标准》(GB/T 14848-1993),对大连市中心城区2006-2010年地下水"三氮"监测资料进行分析。结果表明,"三氮"的检出率(最高为100%)和超标率(最高为58.8%)都较高,其中亚硝酸盐氮超标显著。氨氮浓度各年均值均超标,呈波动变化;亚硝酸盐氮浓度各年均值均超标,并呈上升趋势;硝酸盐氮各年均值波动变化,总体呈上升趋势。除2009年以外,其他4年中丰水期的硝酸盐氮浓度都比枯水期高;除2007年外,其他4年中的丰水期氨氮浓度都比枯水期低。"三氮"最高浓度值主要出现在中心城区周边区域。     

Hybrid Neural Networks and Boosted Regression Tree Models for Predicting Roadside Particulate Matter

This paper examines the application of artificial neural network (ANN) and boosted regression tree (BRT) methods in air quality modelling. The methods were applied to developing air quality models for predicting roadside particle mass concentration (PM₁₀, PM_2.5) and particle number counts (PNC) based on air pollution, traffic and meteorological data from Marylebone Road in London. Elastic net, Lasso and principal components analysis were used as feature selection methods for the ANN models to reduce the number of predictor variables and improve their generalisation. The performance of the ANN with feature selection (ANN hybrid) and the BRT models was evaluated and compared using statistical performance metrics. The performance parameters include root mean square error (RMSE), fraction of prediction within a factor of two of the observation (FAC2), mean bias (MB), mean gross error (MGE), the coefficient of correlation (R) and coefficient of efficiency (CoE) values. The input variables selected by the elastic net produced the best performing ANN models. The ANN hybrid produced models performed only slightly better than the BRT models. The R values of the ANN elastic net and BRT models were 0.96 and 0.95 for PM₁₀, 0.96 and 0.96 for PM_2.5 and 0.89 and 0.87 for PNC, respectively. Their corresponding CoE values were 0.72 and 0.70 for PM₁₀, 0.74 and 0.76 for PM_2.5 and 0.81 and 0.71 for PNC respectively. About 80–99% of all the model predictions are within a factor of two of the observed particle concentrations. The BRT models offer more advantages regarding model interpretation and permit feature selection. Therefore, the study recommends the use of BRT over ANN where the model interpretation is a priority.     

汾河河津段主要污染物时间序列分析

基于Mann-Kendall趋势检验法和小波分析法,研究了汾河河津段2004—2013年高锰酸盐指数、氨氮、溶解氧含量时间序列的变化趋势和突变特性。结果表明:高锰酸盐指数和氨氮含量有显著下降趋势,溶解氧含量有显著上升趋势。高锰酸盐指数和氨氮含量在1、2月较大,8、9月较小;溶解氧含量在1、2月较大,6、7月较小。高锰酸盐指数含量在2004—2009年差异较大;氨氮含量在2004—2013年差异均较大;溶解氧含量在2010—2013年差异较大。     

北京市典型重污染过程中PM2.5载带水溶性无机离子污染特征分析

为研究北京地区冬季PM_(2.5)载带的水溶性无机离子组分污染特征,2013年1月在中国环境科学研究院内采用在线离子色谱(URG-9000B,AIM-IC)对PM_(2.5)中水溶性无机离子(SO_4~(2-)、NO_3~-、Cl~-、NH_4~+、Na~+、K~+、Mg~(2+)、Ca~(2+))进行监测与分析。结果表明,采样期间总水溶性无机离子(TWSI)浓度为61.0μg/m~3,其中二次无机离子SO_4~(2-)、NO_3~-、NH_4~+(SNA)占比达72.3%,在PM_(2.5)中占比为40.29%,表明北京市PM_(2.5)二次污染严重。重污染天[NO_3~-]/[SO_4~(2-)]表明,固定源污染较移动源更为显著。三元相图表明,在空气质量为优的情况下,NH_4~+(在SNA中占比为30.3%~65.5%,下同)主要以NH_4NO_3的形式存在,较少比例以(NH_4)_2SO_4存在;严重污染时,NH_4~+(47.3%~77.9%)主要以(NH_4)_2SO_4形式存在,其次以NH_4NO_3的形式存在,其余的NH_4~+以NH_4Cl的形式存在。[NO_3~-]/[SO_4~(2-)]日变化表明,早、晚机动车高峰影响北京重污染发生。     

闽江入海断面溶解无机氮长时间序列分析及入海通量估算

为深入研究闽江口富营养化机制,于1985—2021年在闽江入海断面开展了水质监测。采用结合局部加权回归散点平滑法(LOWESS)的季节性肯达尔检验(SK检验)对断面溶解无机氮(DIN)及其各组分浓度变化趋势进行分析,同时结合水文资料对入海通量进行估算。结果表明:DIN浓度范围为0.728~3.140 mg/L,在37年间整体呈上升趋势,但不显著。各组分中NO₂-N和NH₃-N浓度分别呈显著和极显著下降趋势,而NO₃-N浓度呈极显著上升趋势。DIN组分中NO₂-N和NH₃-N比重不断减小,而NO₃-N比重不断增大,目前已成为DIN的主要组成部分。DIN入海通量范围为3.59×10⁴~14.85×10⁴ t,在37年间缓慢增加,其各组分入海通量长期变化趋势同浓度变化类似。从长期来看,DIN及其各组分浓度的变化趋势主要受流域环境变化及下游福州市含氮废水排放影响,而在短期则受台风、降水等一些突发环境事件的影响较大。     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Monitoring diel dissolved oxygen dynamics through integrating wavelet denoising and temporal neural networks

Diel dissolved oxygen (DO) time series measured continuously using proximal sensors in situ for a temperate lake were denoised using discrete wavelet transform (DWT) with the orthogonal wavelet families of coiflet, daubechies, and symmlet with order of 10. Diel DO time series denoised were modeled using nine temporal artificial neural networks (ANNs) as a function of water level, water temperature, electrical conductivity, pH, day of year, and hour. Our results showed that time-lag recurrent network (TLRN) using denoised data emulated diel DO dynamics better than the best-performing TLRN using the original data, time-delay neural network (TDNN), and recurrent network (RNN). Daubechies basis dealt with diel DO data slightly better than the other bases given its coefficient of determination (r ²?=?87.1 %), while symmlet performed slightly better than the other bases in terms of root mean square error (RMSE?=?1.2 ppm) and mean absolute error (MAE?=?0.9 ppm).     

三峡水库干流氮和磷含量的季节变化

三峡水库成库后对6个水平断面、4个垂直断面采样分析,对水体水质参数、不同化学形态N以及不同物理形态P的水平分布和垂直分布进行了研究。研究结果表明,成库后TP、NH3-N、NO2--N、NO3--N、IN含量范围分别在0.06～0.34、0.01～0.72、0.002～0.100、0.76～2.09、0.855～2.52 mg/L。TP分布为丰水期>平水期>枯水期,受悬浮物沉降的影响,TP在水库区域含量低于上游区。枯水期、平水期水体中TP以可溶解态为主,丰水期以颗粒态为主。NO2--N、NO3--N、IN含量均为平水期>枯水期>丰水期。水体中IN以NO3--N为主,占IN70%～90%。NO2--N比例最低,占IN3.5%以下。表明水体虽受成库影响流速减缓,但水体依然有较强的自净能力。垂直分布上,TP、IN在3个层次变化不显著,未出现分层现象。各采样站点TP、NO3--N、IN通量和流量有显著相关。     

Artificial neural network modeling of dissolved oxygen in reservoir

The water quality of reservoirs is one of the key factors in the operation and water quality management of reservoirs. Dissolved oxygen (DO) in water column is essential for microorganisms and a significant indicator of the state of aquatic ecosystems. In this study, two artificial neural network (ANN) models including back propagation neural network (BPNN) and adaptive neural-based fuzzy inference system (ANFIS) approaches and multilinear regression (MLR) model were developed to estimate the DO concentration in the Feitsui Reservoir of northern Taiwan. The input variables of the neural network are determined as water temperature, pH, conductivity, turbidity, suspended solids, total hardness, total alkalinity, and ammonium nitrogen. The performance of the ANN models and MLR model was assessed through the mean absolute error, root mean square error, and correlation coefficient computed from the measured and model-simulated DO values. The results reveal that ANN estimation performances were superior to those of MLR. Comparing to the BPNN and ANFIS models through the performance criteria, the ANFIS model is better than the BPNN model for predicting the DO values. Study results show that the neural network particularly using ANFIS model is able to predict the DO concentrations with reasonable accuracy, suggesting that the neural network is a valuable tool for reservoir management in Taiwan.     

Spatial and seasonal distribution of nitrogen in marsh soils of a typical floodplain wetland in Northeast China

Horizontal and profile distributions of nitrogen in marsh soils in different seasons were studied in a typical site within the Erbaifangzi wetland in Northeast China. Results showed that there was higher spatial heterogeneity for nitrate nitrogen (NO₃^--_{3}^{-}-N) and ammonium nitrogen (NH₄⁺_{4}^{+}–N), as well as available nitrogen (AN), in surface soils in July compared to that in September. Relative to July, the mean nitrogen contents in surface soils were slightly higher in September; however, in November, soils contained significantly lower NO₃^--_{3}^{-}-N and NH₄⁺_{4}^{+}–N, higher AN, organic nitrogen (Org-N), and total nitrogen (TN). Except for mineral nitrogen, no significant differences were observed between Org-N and TN contents in September and November. Nitrogen contents generally declined exponentially with depth along soil profiles in three sampling dates (July, September, and November), except for a significant accumulation peak of NO₃^--_{3}^{-}-N at the 20–30 cm depth in September. However, NH₄⁺_{4}^{+}–N contents showed a vertical alternation of “increasing and decreasing” in both July and September, while nearly kept constant with depth in November. The depth ranking of nitrogen showed the shallowest distribution for AN, followed by Org-N and TN, while deeper distributions for NO₃^--_{3}^{-}-N and NH₄⁺_{4}^{+}–N. TN, Org-N, and AN were significantly correlated with soil organic matter and total phosphorus. Soil pH values were significantly correlated with TN and AN contents in surface soils. Clay contents showed significant correlations with nitrogen contents except for NO₃^--_{3}^{-}-N in surface soils and NH₄⁺_{4}^{+}–N in profile soils. However, soil moisture was not significantly correlated with nitrogen contents among all soil samples.     

Factors and sources influencing ionic composition of atmospheric condensate during winter season in lower troposphere over Delhi, India

Atmospheric condensate (AC) and rainwater samples were collected during 2010–2011 winter season from Delhi and characterized for major cations and anions. The observed order of abundance of cations and anions in AC samples was NH ₄ ⁺ ?>?Ca²⁺?>?Na⁺?>?K⁺?>?Mg²⁺ and HCO ₃ ^? ?>?SO ₄ ^2? ?>?Cl^??>?NO ₂ ^? ?>?NO ₃ ^? ?>?F^?, respectively. All samples were alkaline in nature and Σ _cation/Σ _anion ratio was found to be close to one. NH ₄ ⁺ emissions followed by Ca²⁺ and Mg²⁺ were largely responsible for neutralization of acidity caused by high NO _x and SO₂ emissions from vehicles and thermal power plants in the region. Interestingly, AC samples show low nitrate content compared with its precursor nitrite, which is commonly reversed in case of rainwater. It could be due to (1) slow light-mediated oxidation of HONO; (2) larger emission of NO₂ and temperature inversion conditions entrapping them; and (3) formation and dissociation of ammonium nitrite, which seems to be possible as both carry close correlation in our data set. Principal component analysis indicated three factors (marine mixed with biomass burning, anthropogenic and terrestrial, and carbonates) for all ionic species. Significantly higher sulfate/nitrate ratio indicates greater anthropogenic contributions in AC samples compared with rainwater. Compared with rainwater, AC samples show higher abundance of all ionic species except SO₄, NO₃, and Ca suggesting inclusion of these ions by wash out process during rain events. Ionic composition and related variations in AC and rainwater samples indicate that two represent different processes in time and space coordinates. AC represents the near-surface interaction whereas rainwater chemistry is indicative of regional patterns. AC could be a suitable way to understand atmospheric water interactions with gas and solid particle species in the lower atmosphere.     

富营养化湖泊叶绿素a时空变化特征及其影响因素分析

基于内蒙古乌梁素湖区20个监测点5、7、9、11月的监测数据,分析水体中叶绿素a浓度时空变化情况。同时,分析水体中总氮、总磷、氨氮、硝酸盐氮、COD、p H、总有机碳与叶绿素a的相关性。结果显示,叶绿素a浓度呈现由西北向东南逐渐减少的趋势,而浓度峰值出现在7月下旬,低值出现在11月下旬。相关因素与叶绿素a的相关性呈复杂性,线性拟合结果显示,与COD没有明显相关性;与总有机碳呈弱负相关性,与p H呈负相关性;而与总磷、总氮、氨氮、硝酸盐氮呈正相关性。期望该研究为干旱区湖泊水体富营养化控制和水资源管理提供科学依据。     

Effects of land use types on surface water quality across an anthropogenic disturbance gradient in the upper reach of the Hun River, Northeast China

Surface water quality is vulnerable to pollution due to human activities. The upper reach of the Hun River is an important water source that supplies 52 % of the storage capacity of the Dahuofang Reservoir, the largest reservoir for drinking water in Northeast China, which is suffering from various human-induced changes in land use, including deforestation, reclamation/farming, urbanization and mine exploitation. To investigate the impacts of land use types on surface water quality across an anthropogenic disturbance gradient at a local scale, 11 physicochemical parameters (pH, dissolved oxygen [DO], turbidity, oxygen redox potential, conductivity, biochemical oxygen demand [BOD₅], chemical oxygen demand [COD], total nitrogen [TN], total phosphorus [TP], NO ₃ ^? -N, and NH ₄ ⁺ -N) of water from 12 sampling sites along the upper reach of the Hun River were monitored monthly during 2009–2010. The sampling sites were classified into four groups (natural, near-natural, more disturbed, and seriously disturbed). The water quality exhibited distinct spatial and temporal characteristics; conductivity, TN, and NO ₃ ^? -N were identified as key parameters indicating the water quality variance. The forest and farmland cover types played significant roles in determining the surface water quality during the low-flow, high-flow, and mean-flow periods based on the results of a stepwise linear regression. These results may provide incentive for the local government to consider sustainable land use practices for water conservation.     

重庆市黔江区降水地球化学特征

为了解生态旅游城市重庆市黔江区大气污染状况,2015年采集了91个降水样品,分析了降水中离子组分分布特征,运用富集因子法、海盐示踪法、相关性分析、主成分分析、聚类分析和HYSPLIT模型分析了降水化学组分来源。研究结果表明:黔江区域降水p H为5.66~6.96,加权平均值为6.34,降水离子组分浓度大小次序为SO_4~(2-)Ca~(2+)NH_4~+Mg~(2+)NO_3~-Cl~-Na~+K~+F~-,SO_4~(2-)、Ca~(2+)之和占总离子的63.95%;除Mg~(2+)和K+外,其余组分离子浓度与总离子浓度随季节变化(冬季春季秋季夏季)呈同样的变化特征。Ca~(2+)、Mg~(2+)和K+大部分均来源于陆源贡献,Na~+可能受到了海洋源的影响,SO_4~(2-)和NO_3~-主要来源于人为输入源的贡献,Cl~-是受土壤物质和海洋的双重影响。轨迹水汽运输结果表明:黔江区域的降水主要受到西北气团、西南季风、西风环流和极地气候共同作用输入。降水中各个离子组分均表现出显著性或极显著性关系,主成分分析结果表明,第一主成分上研究的降水离子组分中都具有相对较大正载荷,第二组分pH、降水量和气温为一类。     

大同市大气颗粒物浓度与水溶性离子季度分布特征

为研究大同市大气颗粒物质量浓度与水溶性离子组成特征,于2013年2、7、9、12月,分别对大同市及其对照点庞泉沟国家大气背景点进行了PM2.5及PM10的采样,通过超声萃取-IC法测定了样品中的9种水溶性离子,结果表明,大同市大气颗粒物污染1、4季度重于2、3季度,PM2.5季度均值全年均未超标,PM10仅第1季度超标1.4倍,污染状况总体良好,PM2.5与PM10相关系数R为0.75,说明大同市颗粒物污染有较为相近的来源,且不同季节均以粗颗粒物为主;大同市PM2.5中水溶性离子浓度分布为SO2-4、NO-3、NH+4Cl-、Ca2+K+、Na+F-、Mg2+,PM10中Ca2+浓度仅次于SO2-4、NO-3,控制扬尘将有效降低PM10的浓度;PM2.5及PM10中的9种水溶性离子在不同季度的浓度与颗粒物浓度分布规律类似,1、4季度较高,2、3季度较低;由阴阳离子平衡计算结果可知,相关性方程的斜率K为1.045,表明大同市大气颗粒物中阳离子相对亏损,大气细粒子组分偏酸性。NO-3与SO2-4浓度比值均小于1,大同市以硫酸型污染为主,大气中的SO2-4主要来源于人类活动排放。     

Chemistry of fogs at Agra, India: Influence of soil particulates and atmospheric gases

Fog water samples were collected in the months of December and January during 1998–2000 at Agra, India. The samples were analyzed for pH, major anions (F⁻, Cl⁻, SO₄ ²⁻, NO₃ ⁻, HCOO⁻ and CH₃COO⁻), major cations (Ca²⁺, Mg²⁺, Na⁺ and K⁺) and NH₄ ⁺ using ion chromatography, ICP-AES and spectrophotometer methods, respectively. pH of fog water samples ranged between 7.0 and 7.6 with a volume weighted mean of 7.2, indicating its alkaline characteristic. NH₄ ⁺ contributed 40%, SO₄ ²⁻ and NO₃ ⁻ accounted for 28%, while Ca²⁺, Mg²⁺, Na⁺ and K⁺ accounted for 16% of the total ionic concentration. The ratios of Mg²⁺/Ca²⁺ and Na⁺/Ca²⁺ in fog water indicates that 50–75% of fog water samples correspond to the respective ratios in local soil. Significant correlation between Ca²⁺, Mg²⁺, Na⁺ and K⁺ suggests their soil origin. The order of neutralization, NH₄ ⁺ (1.4) > Ca²⁺ (0.28) > Mg²⁺ (0.12), indicates that NH₄ ⁺ is the major neutralizing species. Fog water and atmospheric alkalinity were also computed and were found to be 873 and 903 neqm⁻³, respectively. Both of these values are higher than values reported from temperate sites and thus indicate that at the present level of pollutants, there is no risk of acid fog problem. The study also shows that the alkaline nature of fog water is due to dissolution of ammonia gas and partly due to interaction of fog water with soil derived aerosols.     

2010—2019年台州市市区降水化学组成特征及变化趋势

为了解台州市市区大气降水化学成分组成特征及变化规律,对2010—2019年台州市市区降水监测数据进行了统计分析。结果表明:2010—2019年降水样品pH为4.20～4.84夏高冬低,强酸性降水频率下降显著,电导率平均值为3.16 mS/cm。SO_4~(2-)和NO_3~-是降水中最主要的阴离子,NH_4~+和Ca~(2+)是降水中最主要的阳离子。Ca~(2+)浓度在2018年开始有所抬升,SO_4~(2-)和NO_3~-浓度整体呈波动下降趋势。SO_4~(2-)与NO_3~-浓度比均值为1.50,呈下降趋势,同大气中SO_2与NO_2的质量浓度比变化趋势基本一致。SO_4~(2-)和NO_3~-相关性显著,Cl~-、Na~+及Mg~(2+)三者之间具有较好相关性。降水与气态污染物相关性不大,对颗粒物有明显冲刷去除作用。SO_2和NO_x的排放量显著下降,酸雨污染呈现改善过程。     

Denitrification potential of the Northern Arabian Gulf—an experimental study

The study aims to establish denitrification potential of the Northern Arabian Gulf (NAG), as nitrogen critically affects the ocean productivity, obliterates acidity, oxidative capacity and radiative transfer capability of atmosphere. The experimental study was conducted by taking cores from intertidal zones from two different sites in North and South, referred as sites N and S; representing two distinct environmental milieu. The experiment was conducted in controlled laboratory conditions simulating the tidal cycles. Multiple cores were taken and loaded with seawater with different N concentrations, the redox potential was established for each condition. Redox potential was significantly lower at 10?cm depth compared to the surface in all cores (P?<?0.001). The redox potential at surface and at 10?cm depth was significantly lower at site S compared to site N (P?<?0.001; F?=?714.2), suggesting anaerobic sediments at site S. Effects of nitrate spiked seawater on denitrification under nonflooded and flooded conditions at the two sites were also studied. Three-way ANOVA analysis indicated that site, nitrate concentration, and flooding had significant main and interactive effects on the rate of denitrification. The results suggest that under ambient nitrate concentrations (0.03?mg NO₃-N?l^?1), 6.3?±?2.1?g NO₃-N?ha?day can be denitrified by inter-tidal zone sediments. At a nitrate concentration of 1?mg NO₃-N?l^?1, 92?±?16?g NO₃-N?ha?day may be denitrified whilst at a very high nitrate load of 10?mg NO₃-N?l^?1, the sediments may attain a rate of denitrification close to 404?±?78?g NO₃-N?ha?day.     

Study on concentration of ambient NH 3 and interactions with some other ambient trace gases

We present diurnal variation of ambient ammonia (NH₃) in relation with other trace gases (O₃, CO, NO, NO₂, and SO₂) and meteorological parameters at an urban site of Delhi during winter period. For the first time, ambient ammonia (NH₃) was monitored very precisely and continuously using ammonia analyzer, which operates on chemiluminescence method. NH₃ estimation efficiency of the chemiluminescence method (>90%) is much higher than the conventional chemical trapping method (reproducibility 4.5%). Ambient NH₃ concentration reaches its maxima (46.17 ppb) at night and minimum during midday. Result reveals that the ambient ammonia (NH₃) concentration is positively correlated with ambient NO (r ²?=?0.79) and NO₂ (r ²?=?0.91) mixing ratio and negatively correlated with ambient temperature (r ²?=???0.32). Wind direction and wind speed indicates that the nearby (~500 m NW) agricultural fields may be major source of ambient NH₃ at the observational site.