Stable carbon and nitrogen isotopic characteristics of PM2.5 and PM10 in Delhi,India

This study presents the chemical composition (carbonaceous and nitrogenous components) of aerosols (PM_2.5 and PM₁₀) along with stable isotopic composition (δ¹³C and δ¹⁵N) collected during winter and the summer months of 2015–16 to explore the possible sources of aerosols in megacity Delhi, India. The mean concentrations (mean?±?standard deviation at 1σ) of PM_2.5 and PM₁₀ were 223?±?69 µg m^?3 and 328?±?65 µg m^?3, respectively during winter season whereas the mean concentrations of PM_2.5 and PM₁₀ were 147?±?22 µg m^?3 and 236?±?61 µg m^?3, respectively during summer season. The mean value of δ¹³C (range: ??26.4 to ??23.4‰) and δ¹⁵N (range: 3.3 to 14.4‰) of PM_2.5 were ??25.3?±?0.5‰ and 8.9?±?2.1‰, respectively during winter season whereas the mean value of δ¹³C (range: ??26.7 to ??25.3‰) and δ¹⁵N (range: 2.8 to 11.5‰) of PM_2.5 were ??26.1?±?0.4‰ and 6.4?±?2.5‰, respectively during the summer season. Comparison of stable C and N isotopic fingerprints of major identical sources suggested that major portion of PM_2.5 and PM₁₀ at Delhi were mainly from fossil fuel combustion (FFC), biomass burning (BB) (C-3 and C-4 type vegitation), secondary aerosols (SAs) and road dust (SD). The correlation analysis of δ¹³C with other C (OC, TC, OC/EC and OC/WSOC) components and δ¹⁵N with other N components (TN, NH₄⁺ and NO₃^?) are also support the source identification of isotopic signatures.

     

Atmospheric ionic species in PM2.5 and PM1 aerosols in the ambient air of eastern central India

This study elucidates the characteristics of ambient PM_2.5 (fine) and PM₁ (submicron) samples collected between July 2009 and June 2010 in Raipur, India, in terms of water soluble ions, i.e. Na⁺, NH ₄ ⁺ , K⁺, Mg²⁺, Ca²⁺, Cl^?, NO ₃ ^? and SO ₄ ^2? . The total number of PM_2.5 and PM₁ samples collected with eight stage cascade impactor was 120. Annual mean concentrations of PM_2.5 and PM₁ were 150.9?±?78.6 μg/m³ and 72.5?±?39.0 μg/m³, respectively. The higher particulate matter (PM) mass concentrations during the winter season are essentially due to the increase of biomass burning and temperature inversion. Out of above 8 ions, the most abundant ions were SO ₄ ^2? , NO ₃ ^? and NH ₄ ⁺ for both PM_2.5 and PM₁ aerosols; their average concentrations were 7.86?±?5.86 μg/m³, 3.12?±?2.63 μg/m³ and 1.94?±?1.28 μg/m³ for PM_2.5, and 5.61?±?3.79 μg/m³, 1.81?±?1.21 μg/m³ and 1.26?±?0.88 μg/m³ for PM₁, respectively. The major secondary species SO ₄ ^2? , NO ₃ ^? and NH ₄ ⁺ accounted for 5.81%, 1.88% and 1.40% of the total mass of PM_2.5 and 11.10%, 2.68%, and 2.48% of the total mass of PM₁, respectively. The source identification was conducted for the ionic species in PM_2.5 and PM₁ aerosols. The results are discussed by the way of correlations and principal component analysis. Spearman correlation indicated that Cl^? and K⁺ in PM_2.5 and PM₁ can be originated from similar type of sources. Principal component analysis reveals that there are two major sources (anthropogenic and natural such as soil derived particles) for PM_2.5 and PM₁ fractions.     

克拉玛依市PM2.5质量浓度分布及其影响因素分析

利用2015年1月至2017年12月中国环境监测总站全国城市空气质量实时发布平台中公布的克拉玛依5个监测点数据和同时期克拉玛依国家基本气象站的观测数据,分别研究了克拉玛依市4个行政区的PM_2.5浓度的时空变化特征以及气象条件对克拉玛依PM_2.5浓度变化的影响。结果表明：从月份上看,克拉玛依每年的1月、2月、12月PM_2.5浓度最高,3月、11月PM_2.5浓度较高,其中,独山子每年2月的PM_2.5浓度均最高,2016年2月独山子PM_2.5平均浓度最高,达到134 μg·m^-3,超过国家一级标准值的2.8倍,属于中度污染,从季节上看,克拉玛依四季PM_2.5浓度变化呈现波峰波谷变化趋势,表现为冬季最高,春季次之,夏季、秋季各区变化不一的特点,采暖期的PM_2.5浓度高于非采暖期的PM_2.5浓度;克拉玛依PM_2.5浓度在空间上的总体分布为：独山子区>白碱滩区>克拉玛依区>乌尔禾区;从风向、风速、气温、气压和相对湿度等气象要素与PM_2.5浓度的相关性来看,气压、相对湿度与PM_2.5浓度呈显著正相关,气温、风速、风向与PM_2.5浓度呈负相关,其中气温、风向与PM_2.5浓度呈显著负相关。     

天津大气能见度与相对湿度、PM10及PM2.5的关系

为研究大气气溶胶及空气中水汽与大气能见度下降的关系,利用2009年天津大气边界层观测站大气能见度资料和同期观测的相对湿度、PM₁₀及PM_2.5资料,对三者与大气能见度的关系进行了分析。结果表明：大气能见度与相对湿度线性相关系数最高,PM2.5次之;大气能见度随相对湿度的增大而明显降低。相对湿度低于60 %时,大气能见度与PM_2.5的非线性相关性较好,与PM₁₀次之,与PM₁₀与PM_2.5差值的相关性最差。相对湿度高于60 %时,大气能见度与PM₁₀的非线性相关性较好,与PM₁₀-PM_2.5差值的相关性次之。大气能见度与相对湿度非线性相关系数高于线性相关系数。利用相对湿度、PM₁₀及PM_2.5数据计算得到了具有季节变化的非线性大气能见度拟合公式,经验证,该公式能较好地模拟天津地区的大气能见度。     

Correlation between black carbon aerosols, carbon monoxide and tropospheric ozone over a tropical urban site

Black carbon aerosols plays an important role in the earth's radiative balance and little is known of their concentrations, distributions, source strength, and especially the aerosol chemistry of the developing world. The present study addresses the impact of back carbon aerosols on different atmospheric species like CO and tropospheric ozone over an urban environment, namely Hyderabad, India. Ozone concentration varies from 14 to 63 ppbv over the study area. Diurnal variations of ozone suggest that ozone concentration starts increasing gradually after sunrise, attaining a maximum value by evening time and decreasing gradually thereafter. Black carbon (BC) aerosol mass concentrations varies from 1471 to 11,175 ng m⁻³. The diurnal variations of BC suggest that the concentrations are increased by a factor of 2 during morning (06:00–09:00 h) and evening hours (18:00 to 22:00 h) compared to afternoon hours. Positive correlation has been observed between BC and CO (r²=0.74) with an average slope of 6.4×10⁻³ g BC/g CO. The slope between black carbon aerosol mass concentration and tropospheric ozone suggests that every 1 μg m⁻³ increase in black carbon aerosol mass concentration causes a 3.5 μg m⁻³ reduction in tropospheric ozone. The results have been discussed in detail in the paper.     

Northwest Pacific tropical cyclone activity and July rainfall over India

Summary In 2002, India had experienced one of the most severe droughts. The severe drought conditions were caused by the unprecedented deficient rainfall in July 2002, in which only 49% of the normal rainfall was received. One of the major circulation anomalies observed during July 2002, was the active monsoon trough over Northwest (NW) Pacific and enhanced typhoon activity over this region. The present study was designed to examine the long-term relationships between Tropical Cyclone (TC) activity over NW Pacific and monsoon rainfall over India in July. A statistically significant negative correlation between TC days over NW Pacific and July rainfall over India was observed. Spatial dependence of the relationship revealed that TCs forming over NW Pacific east of 150° E and moving northwards have an adverse effect on Indian monsoon rainfall. It was observed that TCs forming over the South China Sea and moving westwards may have a positive impact on monsoon rainfall over India. Enhanced TC activity over NW Pacific during July 2002 induced weaker monsoon circulation over the Indian region due to large-scale subsidence.     

基于观测、模拟和同化数据的PM2.5污染回顾分析

基于观测数据空间插值、数值模拟以及最优插值同化方法构建了京津冀地区PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)空间插值数据、模拟数据和同化数据,并首次比较分析了三种数据在PM2.5污染回顾分析上的应用潜力和优缺点。针对2014年2月19~28日京津冀地区PM2.5污染过程的分析发现:(1)观测空间插值数据难以完整表征PM2.5污染的时空演变特征,在没有观测覆盖区域误差较大,容易出现虚假的高低值中心;(2)模拟数据具有较高时空分辨率,对PM2.5污染时空演变特征描述更加细致,但在这次污染过程中仍存在较大不确定性,其均方根误差大于100μg/m3;(3)同化数据不仅能对PM2.5空间分布特征进行细致描述,其数据精度在独立验证站点也显著高于模拟数据,其均方根误差比模拟数据低约50%,与站点观测数据的相关系数也比模拟数据高0.2以上。基于PM2.5同化数据,对这次京津冀PM2.5污染过程的时空演变特征进行了详细回顾分析,发现这次污染过程存在自京津冀南部PM2.5污染累积并向北输送发展的生成特点,消亡过程为风向转换下自北向南清除,造成京津冀南部城市先污染后清除,北部城市后污染先清除,并且有慢累积、快清除的特征。从发展演变过程中污染所占空间面积来看,25日PM2.5污染范围最大,覆盖模式第三区域60.5%面积。     

2015年11月沈阳地区一次PM2.5重污染过程综合分析

利用多源观测资料综合分析了2015年11月沈阳地区一次PM2.5 重污染天气的气象条件、垂直风场演变、大气边界层特征以及污染物的来源。结果表明:本次重污染过程中,沈阳市区PM2.5浓度长达81h超过250μg · m^-3 ,其中峰值浓度达到1287μg · m^-3 ,重污染期间PM2.5 /PM10 的比例最高为90%。受地面倒槽和黄淮气旋影响,近地面层持续存在的逆温层、高相对湿度和弱偏北风为颗粒物吸湿增长和长时间聚集提供有利的天气条件。风廓线雷达风场资料显示在重污染期间,近地面层存在弱风速区、凌乱风场和弱下沉气流。利用风廓线雷达资料计算了边界层通风量(Ventilation Index,VI)和局地环流指数(Recirculation,R),边界层通风量VI和PM2.5 存在明显的负相关,非污染日VI是重污染日的2倍,局地环流指数R在重污染天气前大于0.9,而在污染期间部分空间R小于0.8。通过后向轨迹模式和火点监测资料分析发现,沈阳上空300m高度气团来自于生物质燃烧区域,而且沈阳地区NO2和CO浓度的变化与PM2.5一致,说明本次重污染过程也可能和生物质燃烧有关。     

Evaluation of an urban land surface scheme over a tropical suburban neighborhood

A two-step statistical downscaling method has been reviewed and adapted to simulate twenty-first-century climate projections for the Gulf of Fonseca (Central America, Pacific Coast) using Coupled Model Intercomparison Project (CMIP5) climate models. The downscaling methodology is adjusted after looking for good predictor fields for this area (where the geostrophic approximation fails and the real wind fields are the most applicable). The method’s performance for daily precipitation and maximum and minimum temperature is analysed and revealed suitable results for all variables. For instance, the method is able to simulate the characteristic cycle of the wet season for this area, which includes a mid-summer drought between two peaks. Future projections show a gradual temperature increase throughout the twenty-first century and a change in the features of the wet season (the first peak and mid-summer rainfall being reduced relative to the second peak, earlier onset of the wet season and a broader second peak).     

Projection of PM2.5 and Ozone Concentration Changes over the Jing-Jin-Ji Region in China

Based on GISS-E2-R model simulations, the changes in PM2.5 and ozone concentrations during 2016– 35 are analyzed over the Jing-Jin-Ji region under different future emissions scenarios: 2.6, 4.5, 6.0, 8.5 Representative Concentration Pathways scenarios(RCP2.6, RCP4.5, RCP6.0, and RCP8.5), compared to the baseline periods of 1851–70(pre-industrial) and 1986–2005(present day). The results show that PM2.5 increases under all emissions scenarios, with the maximum value occurring in the southeastern part of the region under most scenarios. As for ozone, its concentration is projected to increase during 2016–35 under all emissions scenarios, compared to the baseline periods. The temporal evolutions of PM2.5 and ozone show PM2.5 reaching a peak during 2020–40, while ozone will likely increase steadily in the future.     

Variations in CO,O<Subscript>3</Subscript> and black carbon aerosol mass concentrations associated with planetary boundary layer (PBL) over tropical urban environment in India

Carbon monoxide (CO), Ozone (O₃) and Black Carbon (BC) aerosol mass concentrations in relation to planetary boundary layer (PBL) height measurements were analyzed from January–December, 2008 over tropical urban environment of Hyderabad, India. DMSP-OLS night-time satellite data were analyzed for fire occurrence over the region and its correlation with pollution concentrations over the urban region. Results of the study suggested considerable increase in CO and BC concentrations during early morning hours. Higher concentration of BC, CO and ozone was observed during pre-monsoon, post-monsoon and winter and lowest concentrations exhibited during monsoon season. NCEP/NCAR reanalysis winds suggested long range transport of aerosols and trace gases from forest fires are enhancing the pollutant concentrations over the study area.     

乌鲁木齐市PM10,PM2.5和PM1.0浓度及分布变化特征研究

利用GRIMM180气溶胶粒谱分析仪采集乌鲁木齐市PM10、PM2.5和PM1.0数据,研究表明:乌鲁木齐市气溶胶颗粒物质量浓度在进入采暖季后急剧增加,冬季颗粒物中细粒子含量最高,PM2.5/PM10可达77.6%,PM2.5/PM10,PM1.0/PM10,PM1.0/PM2.5三比值体现了颗粒物的分布特征,四季污染程度越高,细粒子含量越高。四季无降水日PM10、PM2.5、PM1.0的质量浓度和分布的日变化基本呈三峰三谷型,出现早—午—晚峰值,上午—下午—午夜后谷值,各季节峰谷值具体出现时间略有差别,由于冬季逆温层顶盖等因素的影响,冬季质量浓度和分布的日变化在此基础上多了两次波动。降水的发生对冬、春季质量浓度的影响大于夏、秋季,对不同粒径段粒子的分布影响有一定差别。     

曹妃甸工业区空气污染来源及其对周边地区污染贡献的数值模拟研究

为了深入研究曹妃甸工业区的建立和大型工业企业的迁入对京津冀地区空气质量的影响,利用嵌套网格空气质量预报模式系统NAQPMS(Nested Air Quality Prediction Modeling System)研究该工业区和周边地区在2016年秋、冬季空气质量状况,并对PM2.5(空气动力学当量直径小于等于2.5μm的颗粒物,即细颗粒物)来源与区域输送进行分析。结果表明:模式能很好地再现气象要素和污染物浓度分布特征;曹妃甸地区本地排放在1月和10月的月均贡献分别为17.8%和25.8%。当空气质量为优良时,曹妃甸地区PM2.5主要受短距离周边传输影响,唐山和天津贡献率之和达23%～53%;当空气出现轻度及以上污染时,曹妃甸地区PM2.5浓度主要受到长距离输送的影响,河北中南部和山东地区贡献之和达40%～50%。曹妃甸工业园区排放对周边地区PM2.5浓度贡献相对较小,对唐山和天津地区贡献为3%～7%,对京津冀地区其他城市PM2.5浓度贡献可忽略不计。空气质量转差时,曹妃甸、北京和天津地区PM2.5中一次排放占比相较于空气质量优良时明显下降,二次生成的无机盐类和二次有机气溶胶贡献率增加;曹妃甸地区10月二次生成硫酸盐贡献率较1月明显增加,月均贡献率为22%。因此,在致力于削减京津冀地区PM2.5一次排放的同时,对SO2、NOx等进行控制,能有效改善该地区空气质量。     

In-cloud and below-cloud scavenging of aerosol ionic species over a tropical rural atmosphere in India

The temporal variation in concentrations of major water soluble ionic species has been studied from several rain events occurred over Gadanki (13.5 °N, 79.2 °E), located in tropical semi arid region in southern India. The contribution from rain-out (in cloud) and wash-out (below cloud) processes to the total removal of ionic species by rain events is also estimated using the pattern of variations of ionic species within an individual event. A number of rain samples were collected from each rain event during June–November in 2006, 2007 and 2008. On average, nearly 20% of the total NH ₄ ⁺ and non-sea SO ₄ ^2? is removed by in-cloud scavenging, suggesting that their removal by “below cloud” washout is relatively dominant. In contrast Na⁺, Ca²⁺, Mg²⁺, NO ₃ ^? and sea-SO ₄ ^2? are mainly removed by below-cloud scavenging or wash-out process. A significant variation in the acidity was observed within rain events with successive precipitation showing higher acidity at the final stage of the precipitation due to partial neutralization of non-sea SO ₄ ^2? . Overall, greater influence of both terrestrial and anthropogenic sources is recorded in the rain events compared to that from marine sources.     

中国的降水量符合正态分布吗?

利用1961—2017年长江流域700个气象站点逐月降水资料计算长江流域9个子流域面雨量,采用基于Box-Cox正态分布转换后的百分位法对长江流域不同时间长度的极端降水气候事件阈值进行界定。结果表明,在数据序列长度发生变化的情况下,面雨量序列经Box-Cox正态转换后,计算得到的极端降水气候事件阈值的变化相较于常规百分位法明显减小,具有更为稳健的特性,从而使得相应极端降水气候事件个例的挑选更为稳定。根据该方法得到的阈值,对2018年汛期(6—8月)长江各子流域极端降水气候事件进行判定,岷沱江流域发生了极端多雨气候事件,而长江干流重庆-宜昌段、汉江及中游干流区间发生了极端少雨气候事件。

     

Distributions of ozone and related trace gases at an urban site in western India

Continuous in-situ measurements of surface ozone (O₃), carbon monoxide (CO) and oxides of nitrogen (NOx) were conducted at Udaipur city in India during April 2010 to March 2011. We have analyzed the data to investigate both diurnal and seasonal variations in the mixing ratios of trace gases. The diurnal distribution of O₃ showed highest values in the afternoon hours and lower values from evening till early morning. The mixing ratios of CO and NOx showed a sharp peak in the morning hours but lowest in the afternoon hours. The daily mean data of O₃, CO and NOx varied in the ranges of 5–51 ppbv, 145–795 ppbv and 3–25 ppbv, respectively. The mixing ratios of O₃ were highest of 28 ppbv and lowest 19 ppbv during the pre-monsoon and monsoon seasons, respectively. While the mixing ratios of both CO and NOx showed highest and lowest values during the winter and monsoon seasons, respectively. The diurnal pattern of O₃ is mainly controlled by the variations in photochemistry and planetary boundary layer (PBL) depth. On the other hand, the seasonality of O₃, CO and NOx were governed by the long-range transport associated mainly with the summer and winter monsoon circulations over the Indian subcontinent. The back trajectory data indicate that the seasonal variations in trace gases were caused mainly by the shift in long-range transport pattern. In monsoon season, flow of marine air and negligible presence of biomass burning in India resulted in lowest O₃, CO and NOx values. The mixing ratios of CO and NOx show tight correlations during winter and pre-monsoon seasons, while poor correlation in the monsoon season. The emission ratio of ?CO/?NOx showed large seasonal variability but values were lower than those measured over the Indo Gangetic Plains (IGP). The mixing ratios of CO and NOx decreased with the increase in wind speed, while O₃ tended to increase with the wind speed. Effects of other meteorological parameters in the distributions of trace gases were also noticed.     

Study of seasonal variation of PM2.5 concentration associated with meteorological parameters at residential sites in Delhi,India

The seasonal variation of particulate matter and its relationship with meteorological parameters were measured at five different residential sites in Delhi. Sampling was carried out for one year including all three seasons (summer, monsoon, and winter). The yearly average concentration of particulate matter (PM_2.5) was 135.16 ± 41.34 µg/m³. The highest average values were observed in winter (208.44 ± 43.67 µg/m³) and the lowest during monsoon season (80.29 ± 39.47 µg/m³). The annual average concentration of PM_2.5 was found to be the highest at the Mukherjee Nagar site (242.16 µg/m³ ) during the winter and lowest at (Jawaharlal Nehru University) JNU (35.65 µg/m³) during the monsoon season. The strongest correlation between PM mass and a meteorological parameter was a strong negative correlation with temperature (R2=0.55). All other parameters were weakly correlated (R2<0.2) with PM mass.