THE EFFECTS OF DIURNAL MIXING ON THERMAL STRATIFICATION OF STATIC IMPOUNDMENTS1

ABSTRACT: The time variation in the temperature distribution of a static water impoundment is predicted. The body of water is modeled as a discrete number of horizontally isothermal layers and the energy equation is solved using an implicit numerical scheme. Vertical energy transport mechanisms included are solar absorption, molecular diffusion, and convective mixing due to nocturnal turnover. The latter mechanism, called diurnal mixing, is found to have a profound effect on the stratification, particularly in the eplimnion patterns for a typical deep static impoundment.     

Using dispersion and mesoscale meteorological models to forecast pollen concentrations

This work describes the results of research into a source-oriented pollen concentration forecasting technique. Tests were conducted using the National Center for Atmospheric Research/ Penn State Fifth Generation Mesoscale Model (MM5), the National Oceanographic and Atmospheric Administration (NOAA) Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT_4) Model combined with the locations of oak trees and their aerial coverage from biogenic emissions land cover database version 3.1 (BELD3). Daily forecasts of pollen concentrations via MM5 and HYSPLIT_4 were made with 30-min increments and tested against 30-min oak pollen data collected by the St. Louis County Department of Health in Clayton, Missouri, for the month of April 2000.Results from these tests show that the combination of MM5 and HYSPLIT_4 with accurate source locations can provide short-term forecasts as indicated by the levels of forecast pollen and actual oak pollen levels, which follow similar profiles for the day. From the 30 individual pollen concentration forecasts, two example forecasts are presented. Additional studies need to be conducted to further validate these results, using an array of pollen collectors. A better understanding of the biology of pollen release is critical to improving these pollen concentration forecasts.     

Boundary Layer Evolution and its Influence on Ground-Level Ozone Concentrations

Accurate determination of the planetary boundary layer (PBL) height (i.e., mixing height (MH)) is critical to properly simulating pollutant levels with the grid-based photochemical models. In this study, the daytime mixing heights based on the parcel and bulk Richardson number methods are compared with those obtained directly from a numerical mesoscale meteorological model in an effort to evaluate the uncertainties in the estimation of the PBL evolution. Mixing heights are estimated from hourly outputs of meteorological variables of the Penn State/NCAR Mesoscale Model Version 3.3 (MM5V3) with two PBL schemes (Blackadar and Gayno-Seaman) during July 1999 over Philadelphia, PA. An analysis of the diurnal variation in the urban PBL and its influence on ground-level ozone (O₃) levels is presented in this paper. The results indicate that on average, the MHs determined from the bulk Richardson number were larger than those estimated from the parcel method. The MHs from the MM5V3 output were much smaller than those derived from the parcel and bulk Richardson number methods, especially for the Gayno-Seaman scheme that is based on turbulent kinetic energy. The MH and ground-level O₃ concentration have been found to be twice as much on episode days than on non-episode days. The average hourly MH growth rate and O₃ tendency (i.e. rate of change in O₃) were largest during the morning hours (0700 to 1000 eastern standard time (EST)), suggesting that vertical mixing contributes significantly to the accumulation of ground-level O₃ in urban areas in the morning hours.     

Beryllium Concentrations in Ambient Air and Its Source Identification

Beryllium concentrations in atmospheric particulate and soil samples in and around a Beryllium Processing Facility (BPF) have been measured. The mean air concentration level of beryllium in and around the fence line of the BPF is 0.48 ± 0.43 ng m^-3 (n = 397) and is mostly influenced by diurnaland seasonal changes. The observed air concentration levelswere well below the prescribed ambient air quality (AAQ)standard of 10 ng m^-3. The soil concentration levels ofberyllium in the study area were found to be in the range of 1.42–2.75 g g^-1. The mass median aerodynamic diameter (MMAD)of beryllium aerosols in ambient air was found to be 6.9 m.Source identification using the Enrichment Factor (EF) approachindicates soil as the predominant contributory source for air concentrations at the site.     

北京大气CH4、CO2、TOC日变化规律及垂直分布的自动连续观测

提出了一种能自动连续监测CH4、CO2、TOC日变化及垂直分布的系统,利用该系统监测了2002年冬季CH4、CO2、TOC垂直分布的日变化的变化趋势.结果发现,距地面高度增加,受湍流扩散的影响,CH4、CO2、TOC浓度降低.冬季CH4浓度的日变化呈现明显的单峰周期变化,CO2日变化呈双峰形分布,TOC日变化没有明显的特征,其日变化受机动车尾气排放的影响很大.根据2002年冬季CO2浓度与2000年以前的对比结果发现,北京市冬季燃煤排放的污染物已处于一个相对稳定期,而随着北京市机动车保有量迅速增加,尾气排放成为影响某些大气微量气体日变化的主要因素.     

北京市城区春季大气挥发性有机物污染特征

"2+26"城市联防联控措施的实施及北京市产业结构的调整,使得北京市大气中VOCs（volatile organic compounds,挥发性有机物）质量浓度、组成特征及来源发生了变化.运用AirmoVOC（GC-866）在线自动监测仪对2017年3-5月北京市城区大气中的VOCs进行观测.结果表明:①北京市城区春季大气中ρ（TVOCs）（TVOCs为总挥发性有机物）为34.36 μg/m3,ρ（烷烃）、ρ（芳香烃）、ρ（烯烃）、ρ（炔烃）分别占ρ（TVOCs）的57.13%、33.18%、7.54%、2.15%.质量浓度最高的前3位VOCs物种分别为苯、丙烷和乙烷,其质量浓度分别为5.97、3.51、2.63 μg/m3.②ρ（TVOCs）的日变化有3个较明显的峰值,分别出现在05:00、11:00和23:00,ρ（TVOCs）最低值出现在18:00,并且夜间ρ（TVOCs）高于白天.VOCs日变化特征表明,北京市VOCs污染受凌晨时段柴油车尾气排放和早晚交通高峰期汽油车尾气排放的影响较为明显.③春季VOCs的OFP（ozone formation potential,臭氧生成潜势）分析表明,芳香烃对OFP的贡献率（44.22%）最大,其次是烯烃（31.06%）,最后是烷烃（23.86%）;北京市VOCs污染的关键活性组分是丙烯、正丁烷、环戊烷、苯、甲苯、二甲苯.④PMF（正矩阵因子分析法）分析表明,溶剂使用源是北京市春季大气中VOCs最主要的排放源,对TVOCs的贡献率为39.06%,其次是移动源（33.79%）和油气挥发源（17.85%）,燃烧源的贡献率（9.30%）最低.研究显示,控制移动源、溶剂使用源和燃烧源的排放是控制北京市环境空气中VOCs污染的关键.      

菏泽市冬季PM2.5中二元羧酸类SOA的昼夜变化特征

为研究菏泽市冬季大气气溶胶中二元羧酸类化合物的昼夜变化特征与形成机制,于2017年冬季(12月)进行为期1个月的PM_(2.5)样品采集,并分析二元羧酸、酮羧酸、α-二羰基化合物及左旋葡聚糖等化学组分.结果表明,菏泽市冬季PM_(2.5)中白天二元羧酸与酮羧酸的总浓度均呈昼高夜低的变化特征,但α-二羰基化合物(二元羧酸的重要前体物)的变化特征却与之相反,表明白天气溶胶的氧化程度比夜晚强.无论在白天还是晚上,草酸(C_2)均是浓度最高的二元羧酸,其次是邻苯二甲酸(Ph)、丁二酸(C_4)和丙二酸(C_3),与其他城市地区的分子组成是相似的.由C_3/C_4的比值与温度(T)间的相关性分析可知,菏泽市冬季有机化合物主要受本地源的影响,而受远源输入的影响很小. C_2与SO_4~(2-)、气溶胶实际酸度(pHIS)的相关性分析表明,C_2主要是在液相中经酸催化的二次氧化反应形成的.因为主要的二元羧酸类化合物(C_2、Gly和mGly)与左旋葡聚糖(Levo)的相关性很强,且K~+/OC的平均比值为0. 06(范围为0. 03～0. 13),所以可以得出二元羧酸类化合物及K~+主要受生物质燃烧的影响.     

大连地区夏季非甲烷烃(NMHC)特征及其来源解析

非甲烷烃(NMHC)是臭氧的重要前体物之一,对臭氧的生成起着决定性的作用.为研究大连地区大气中NMHC的特征和来源,利用在线NMHC监测仪测定大连地区2014年7月和8月的NMHC组成、日变化并计算该地区的臭氧生成潜势,利用PCA受体模型初步估算大连地区NMHC来源.结果表明,大连地区总NMHC年平均浓度(体积分数)分别为80.7×10-9±62×10-9,其中烷烃所占比例最高,占NMHC总浓度的64%,其次是烯烃19%,芳香烃16%,乙炔1%.正癸烷、乙烯、正辛烷是大连地区NMHC中浓度最高的3种物质,部分物种浓度呈现明显的日变化特征.臭氧生成潜势(OFP)结果显示,大连地区芳香烃对该地区OFP的贡献最大,乙烯、间-乙基甲苯和对-乙基甲苯是对该地区OFP贡献最大的物质.利用主成分分析VOCs物种,提取出6个因子,分别归纳为溶剂使用、液化石油气(LPG)、汽车尾气、植物排放、石化炼制加工和老化气团传输.     

基于CMAQ模式和自适应偏最小二乘回归法的中国地区PM2.5浓度动力-统计预报方法研究

采用CMAQ模式和自适应偏最小二乘回归法相结合的动力-统计预报方法,对2014年1—12月全国252个环境监测站的PM_(2.5)浓度逐时预报值进行了滚动订正,分析了订正前后PM_(2.5)浓度的时空变化特征,重点研究该方法在中国不同地区不同季节的适用性.结果表明:CMAQ模式预报的PM_(2.5)浓度年平均和秋冬季季节平均偏差表现为非均匀空间分布特征,即辽宁、山东部分地区、川渝地区及华中、华东、华南大部分地区预报偏高,京津冀和西部大部分地区预报偏低;订正后PM_(2.5)浓度与实测值的空间分布较一致,上述偏高和偏低地区的PM_(2.5)浓度预报误差显著减小;秋冬季PM_(2.5)浓度预报和订正偏差均大于年平均值.全国区域平均PM_(2.5)浓度实测值存在明显的季节变化特征,1—3月和11—12月较大,其他月份较小;PM_(2.5)浓度预报误差较大,多数时刻预报偏低,尤其是1—3月和11—12月偏低较明显;订正后PM_(2.5)浓度与实测值较接近,而且时间变化趋势较一致,秋冬季PM_(2.5)浓度预报和订正偏差亦明显大于春夏季.秋冬季4个重点污染区域中,京津冀地区PM_(2.5)实测浓度的区域平均值较大,川渝地区次之,长三角和珠三角地区较小;珠三角地区PM_(2.5)浓度预报和订正效果较好,川渝和长三角地区次之,京津冀地区相对较差;经滚动订正后,全年和秋冬季时段PM_(2.5)浓度订正值与实测值的相关系数均显著增加,误差显著减小,尤其是秋冬季订正效果较好.川渝地区的订正改进幅度最大,长三角和京津冀地区次之,珠三角地区较小.本文方法均适用于非污染日和污染日全国范围的PM_(2.5)预报浓度订正,两种天气过程PM_(2.5)浓度的订正效果均较好;该方法对于改进京津冀地区污染日的PM_(2.5)浓度预报更有效,其他3个地区非污染日的订正改进效果优于污染日.本文研究结果可为改进空气质量预报、重霾污染天气预警和防治提供新技术途径和科学依据.     

北京城区和郊区本底站大气污染物浓度的多时间尺度变化特征

利用北京城区海淀宝联站(HD)和上甸子本底站(SDZ)2005—2012连续8年的大气污染物(PM_(2.5)、O3、NO2、SO_2和CO)浓度观测数据进行统计分析,揭示北京城区和郊区主要污染物浓度变化特征、超标情况及其差异.主要结论如下:1连续8年北京城区、郊区PM_(2.5)浓度整体呈缓慢下降趋势,但污染水平仍较高.海淀宝联站和上甸子本底站的PM_(2.5)年均浓度从奥运前3年(2005—2007)的平均值87.1μg·m-3和53.4μg·m-3分别下降到奥运后5年(2008—2012)的平均值67.7μg·m-3和42.1μg·m-3.奥运后5年两站PM_(2.5)年均浓度变化不大,其中城区维持在66~70μg·m-3的高浓度水平.城区PM_(2.5)浓度为3级以上的超标日在四季的发生频率相当,4级和5级以上的超标日则多发生在秋、冬季;各季平均日变化趋势均为双峰双谷型,上下班交通高峰期对PM_(2.5)浓度日变化有重要影响.2城区站O3年均浓度前5年(2006—2010)逐年下降,之后浓度开始回升,而本底站O3年均浓度在此期间变化不大,近6年(2007—2012)维持在72.4~76.3μg·m-3.城、郊O3平均日变化均呈单峰型,其中上甸子站峰值出现时刻晚于城区海淀宝联站.32005—2012年北京城区其它气态污染物浓度(NO2、SO_2和CO)总体均呈缓慢下降趋势,但在2012年浓度有所反弹,城区站气态污染物在秋、冬季的平均浓度均显著高于春、夏季.