Dust dynamics in off-road vehicle trails: Measurements on 16 arid soil types, Nevada, USA

Soil analyses and measurements with the Portable In Situ Wind Erosion Laboratory (PI-SWERL) were conducted on 16 soil types in an area heavily affected by off-road vehicle (ORV) driving. Measurements were performed in ORV trails as well as on undisturbed terrain to investigate how ORV driving affects the vulnerability of a soil to emit PM10 (particles < 10 μm), during the driving as well as during episodes of wind erosion. Particular attention is paid to how the creation of a new trail affects those properties of the topsoil that determine its capability to emit PM10. Also, recommendations are given for adequate management of ORV-designed areas. The type of surface (sand, silt, gravel, drainage) is a key factor with respect to dust emission in an ORV trail. Trails in sand, defined in this study as the grain size fraction 63–2000 μm, show higher deflation thresholds (the critical wind condition at which wind erosion starts) than the surrounding undisturbed soil. Trails in silt (2–63 μm) and in drainages, on the other hand, have lower deflation thresholds than undisturbed soil. The increase in PM10 emission resulting from the creation of a new ORV trail is much higher for surfaces with silt than for surfaces with sand. Also, the creation of a new trail in silt decreases the supply limitation in the top layer: the capacity of the reservoir of emission-available PM10 increases. For sand the situation is reversed: the supply limitation increases, and the capacity of the PM10 reservoir decreases. Finally, ORV trails are characterized by a progressive coarsening of the top layer with time, but the speed of coarsening is much lower in trails in silt than in trails in sand or in drainages. The results of this study suggest that, to minimize emissions of PM10, new ORV fields should preferably be designed on sandy terrain rather than in silt areas or in drainages.     

区域大气环境中PM_(2.5)/PM_(10)空间分布研究

提出了一种利用移动监测技术研究区域大气环境中PM2.5/PM10空间分布的方法,并在2004年12月进行了宁波市全市域PM2.5/PM10空间分布的研究.数据显示:相同路径所代表的地区PM2.5和PM10具有很好的相关性,多数路径上PM2.5与PM10数据的相关系数平方在0.95以上,而不同路径上PM2.5与PM10的比值不同.文中给出了宁波市PM2.5/PM10污染的空间分布图,直观地显示出PM2.5/PM10污染的空间分布情况,突出了污染的重点点位和地区.     

城市环境空气中PM_(10)和PM_(2.5)质量浓度分布的均匀性研究

对长沙市环境空气中PM10、PM2.5质量浓度进行自动监测,并统计分析其分布的均匀性。结果表明,在1 d的4个典型时刻以及日内,PM2.5的质量浓度分布总体上较PM10均匀;从月内日均值及2013年1月—10月的月均值变化情况看,PM2.5质量浓度的相对标准偏差(RSD)总体高于PM10,表明PM2.5在长时间尺度上的分布较PM10更不均匀;就功能区分布而言,PM10、PM2.5质量浓度分布的均匀性没有明显的区域差异,两者的变化幅度与功能区类别没有必然联系。     

宁波市区冬季大气颗粒物及其主要组分的污染特征分析

为了更好地研究影响宁波市区环境空气质量的污染物变化特征,于2010年1月20—30日进行了加强监测。研究结果表明,宁波市区大气中PM₁₀和PM_2.5质量浓度较高,其中PM_2.5/PM₁₀为0.5~0.85。对PM₁₀和PM_2.5采样膜分析,水溶性粒子和含碳组分分别占PM₁₀和PM_2.5质量浓度的56.7%和66.9%,其中二次污染的水溶性离子SO₄^2-、NO₃^-和NH₄⁺是PM₁₀和PM_2.5中浓度较高的离子组分;PM_2.5样品中OC与EC的相关性较好,表明OC与EC的来源相对一致,可能主要来自机动车尾气的贡献;但PM₁₀样品中OC与EC的相关性较差,表明其来源相对复杂;其中SOC的浓度占OC的13%~35%,说明宁波市区冬季导致二次污染的光化学反应不活跃。     

北京市主要PM10排放源成分谱分析

对北京市土壤尘、道路扬尘、城市扬尘、建筑施工尘、钢铁尘、煤烟尘等主要PM10无组织排放源和固定源进行采样、分析,建立相应的成分谱数据库,通过对其化学组分分析,确定各类PM10排放源的化学组分特征和标识元素。土壤尘、建筑施工扬尘、钢铁尘、煤烟尘PM10的标识元素分别为Si、Ca、Fe、Al,道路扬尘显示出明显的土壤尘、建筑施工尘和机动车污染的特征,城市扬尘成分谱与道路尘有很强的共线性,具有明显的道路扬尘特征。     

广州市PM_(10)与气象要素的关系分析

广州的PM10污染状况较为严重.PM10是大气颗粒物中对环境和人体健康危害最大的一类,PM10与医院就诊率、呼吸器官疾病发病率乃至死亡率等关系密切.PM10污染与气象条件关系密切,研究气象条件对PM10污染的影响,对改善城市空气质量条件有重要意义.文章利用2001～2004年广州市PM10和同期地面气象要素的监测资料,定量分析PM10与降雨量、相对湿度、平均温度和气压之间的关系:不同等级的降雨对PM10污染均有一定的清除作用;PM.0日平均质量浓度的改变量随着降雨量的增大而增大;1mm降雨量对PM10的清除能力按春、夏、秋、冬依次递增.春、夏、秋三个季节均为当日平均相对湿度低于季平均相对湿度时容易出现PM10污染天气,冬季则相反.春、秋两季均为当日平均气温在季平均值附近徘徊时,较易出现PM10污染,冬季则相反,夏季较少出现PM10污染.较高气压下PM10污染日的出现频率明显高于非污染日.     

Real-time wet scavenging of major chemical constituents of aerosols and role of rain intensity in Indian region

Real-time simultaneous studies on chemical characteristics of rainwater and PM₁₀ aerosols were carried out to understand the scavenging of major chemical components in Indian region. The concentrations of Ca²⁺, NH₄⁺, SO₄²⁻ and NO₃⁻ were observed to be lower in the aerosol samples collected during rain as compared to before and after rain events. The most significant reduction was noticed for Ca²⁺ (74%) during rain which showed highest scavenging ratio (SR) and indicated that below-cloud scavenging is an effective removal process for Ca²⁺ in Indian region. Among non-sea salt components, Ca²⁺ had highest SR at Hyderabad indicating typical characteristics of crustal influence as abundance of calcium carbonate in soil dust has been reported in India. However, the levels of these major chemical components gradually got build-up in due course of time. After rain events, the levels of SO₄²⁻ aerosols were noticed to be substantially higher (more than double) within 24 h. In general, scavenging ratios for all components (except Ca²⁺, NH₄⁺ and K⁺) were higher over BOB as compared to Hyderabad. The maximum fall in aerosol levels (BR minus AR) was observed during continuous and low intensity rain events that did not allow building up of aerosol concentrations.     

温度和水分对中亚热带人工林生态系统呼吸的调控作用

全球气候变化导致的降水格局改变影响陆地生态系统碳收支状况。陆地生态系统所固定的碳主要通过呼吸作用返回到大气中,而温度和水分是调节生态系统呼吸的重要因素。ChinaFLUX千烟洲中亚热带人工林通量站夏季雨热不同季而造成的季节性干旱为探讨温度和水分对生态系统呼吸的调控作用提供了天然的试验条件。研究利用该生态系统2003-2010年涡度相关和常规气象数据,阐述了生态系统呼吸对温度和土壤含水量的响应特征,对比分析了只考虑温度与同时考虑温度和土壤含水量对生态系统呼吸的季节模式和年呼吸量的影响。研究表明,生态系统呼吸的季节变异主要受土壤温度的控制,呈现指数响应特征。但是,在干旱胁迫条件下,土壤含水量对生态系统呼吸的季节变异起到明显的调控作用。参考温度下的生态系统呼吸(R_ref)明显受土壤含水量的影响。生态系统年呼吸量为1 289.4±73.9 gC·m^-2·a^-1,两类模型的估算结果没有显著差异。但是在生态系统的季节变异上,两类模型估算存在显著差异,同时考虑温度与土壤含水量的模型更适合模拟遭受干旱胁迫的生态系统呼吸。     

宝鸡市大气PM_(10)中水溶性物质的组成及特征研究

2008年冬、春季在宝鸡市4个不同功能区采集PM10样品,探讨了PM10中水溶性物质的化学组成、时空分布特征以及来源。结果表明,冬、春季PM10的平均质量浓度分别为(402±100)、(410±160)μg/m3,无明显季节差异,冬季以交通干道区的PM10浓度为最高,而春季则以商贸区的PM10浓度为最高;冬、春季PM10中水溶性有机碳(WSOC)浓度最高值均出现在商贸区,最低值则分别出现在背景点和交通干道区,水溶性无机碳(WSIC)浓度最高值分别出现在交通干道区和商贸区,最低值均出现在背景点;冬、春季PM10中所含大多数无机离子浓度不存在显著空间差异,但不同功能区PM10中无机离子所占质量分数差异较明显;冬、春季PM10中的水溶性物质质量浓度分别为207、151μg/m3,在PM10中所占质量分数分别为51%和40%,其中,冬、春季水溶性物质浓度最高的分别为居民区和商贸区;冬季PM10中WSOC浓度与SO24-、NO3-浓度有较好的相关性,说明冬季PM10中WSOC的主要组分为二次有机气溶胶,而春季PM10中WSOC浓度与SO42-、NO3-浓度的相关性相对较差,这是由于一次有机气溶胶对WSOC的贡献率较冬季显著增大;宝鸡市与北京市大气PM10浓度、PM10中的SO42-、NO3-、NH4+浓度最为接近;广州市大气PM10中的SO42-所占质量分数(14%)要高于北方城市(宝鸡市和北京市均为9%)。     

西宁市环境空气PM10中多环芳烃特征分析

2009年—2010年采暖季、风沙季和非采暖季在西宁市选择了7个环境监测点位采集了大气PM10样品,利用气相色谱-质谱联用技术测定了其中12种多环芳烃的浓度分布特征。结果显示：西宁市采暖期、风沙季和非采暖期三季PM10载带B[a]P的全市平均浓度分别为2.51ng/m3、1.19 ng/m3、1.55ng/m3;PM10载带的B[a]P的浓度与GB3095-1996中规定日平均浓度限值（10ng/m3）相比较,均不超标。