On the Limits to Manage Air-Quality in Glacier Bay

In Glacier Bay National Park, about 95% of the visitors come on board of cruise ships. The National Park Service has the mandate to manage park resources like air quality and visibility, while ensuring visitation. To understand the impact of cruise-ship emissions on the overall concentrations in Glacier Bay, emission-source contribution ratios (ESCR) and the interaction of pollutant from local and/or distant sources were determined using results from four WRF/Chem simulations of the 2008 tourist season (May 15 to September 15). These simulations only differed by the emissions considered: Biogenic emissions only (CLN), biogenic plus activity-based cruise-ship emissions (REF), biogenic plus all anthropogenic emissions except cruise-ship emissions (RETRO), and all aforementioned emissions (ALL). In general, ESCRs differed among pollutants. Interaction between pollutants from cruise-ship emissions and species from other sources including those advected into the bay decreased towards the top of the atmospheric boundary layer. Pollutants from different sources interacted strongest (lowest) in the west arm of the fjord where ships berthed for glacier viewing (in areas of the bay without cruise-ship travel). Pollutant interaction both enhanced/reduced NO2 concentrations by 10% (4 - 8 ppt absolute). Except for ozone, cruise-ship emissions on average governed air quality in the bay. On days with cruise-ship visits, they contributed between 60% and 80% of the bay-wide daily mean SO2 and NO2 concentrations below 1 km height. On days without visits, cruise-ship contributions still reached 40% due to previous visits. Highest cruise-ship ESCRs occurred during stagnant weather conditions. Despite the fact that all coarse particulate matter was due to anthropogenic sources, worst visibility conditions were due to meteorology. The results suggest limits as well as windows for managing air quality and visibility in Glacier Bay.

Analysis and prediction of the influence of energy utilization on air quality in Beijing

This work evaluates the influence of energy consumption on the future air quality in Beijing,using 2000 as the base year and 2008 as the target year.It establishes the emission inventory of primary PM_(10),SO_(2) and NO_(x) related to energy utilization in eight areas of Beijing.The air quality model was adopted to simulate the temporal and spatial distribution of each pollutant concentration in the eight urban areas.Their emission,concentration distribution,and sectoral share responsibility rate were analyzed,and air quality in 2008 was predicted.The industrial sector contributed above 40% of primary PM_(10) and SO_(2) resulting from energy consumption,while vehicles accounted for about 65% of NO_(x).According to the current policy and development trend,air quality in the eight urban areas could become better in 2008 when the average concentrations of primary PM_(10),SO_(2) and NO_(2) related to energy utilization at each monitored site are predicted to be about 25,50 and 51μg/m^(3),respectively.

城市大气污染物来源特征

以北京市为案例,介绍一种包括污染物排放清单建立、环境空气质量模拟及区域环境影响分析等方法在内的城市大气污染物来源特征分析的整体技术方法,从而确定城市大气污染物的排放分布及浓度分布特征、行业排放分担率及浓度贡献、地区排放分担率及浓度贡献以及区域污染对城市环境空气质量影响等多方面特征,为城市大气污染的控制决策提供必要的理论支持。对北京市的研究结果表明:对PM10,扬尘和工业为主要当地污染源;对SO2,采暖和工业为主要当地污染源;对NOx,交通和工业为主要当地污染源;对3种主要大气污染物,石景山、朝阳南等工业区以及老城区对北京市的大气环境造成重要影响,为需要优先控制的地区;北京市周边污染源对北京环境气质量有较大影响,其中RM10的影响最大(高达47 3%),北京市应同时加强对局地污染与区域污染的控制。

北京市能源利用对空气质量的影响分析和预测

为评价能源消耗对未来北京市空气质量的影响,以2000年为基准年,2008年为目标年,建立了北京市城八区能源利用相关的一次PM10、SO2和NOx的排放清单,采用空气质量模型,模拟城八区内各污染物浓度的时空分布,分析其排放和浓度分布特征及行业分担率,并预测2008年的空气质量.结果表明,对于能源消耗排放的一次PM10和SO2,工业排放对其浓度的贡献率在40%以上;机动车排放对NOx浓度的贡献率在65%左右;按照现行的政策和发展趋势,2008年北京市城八区的空气质量将有所好转,与能源利用相关的一次PM10、SO2和NO2在各监测点的年日均浓度分别约为25,50,51μg/m3.

“十三五”期间惠州市大气环境质量改善及其成因分析

2020年惠州大气环境质量综合指数、优良率分别为2.77、97.8%,均达到“十三五”期间最佳水平;与2016年相比,2020年惠州市NO_(2)、PM_(2.5)和PM10质量浓度分别降低了18.1%、7.3%和20%,这是惠州市本地及其周边城市污染源减排、新冠疫情和气象条件改善等因素共同作用的结果。为提升区域大气环境质量精细化管理水平,利用大气环境质量模型模拟分析这些因素对改善惠州市大气环境质量的贡献。基于2016年大气污染源排放清单,模拟结果显示2020年整体气象条件较2016年更有利于大气污染物的控制,可使NO_(2)、PM_(2.5)、PM_(10)和O_(3)质量浓度分别降低2.6%、4.4%、1.7%和0.9%;惠州本地污染源减排措施对降低NO_(2)、PM_(2.5)和PM10质量浓度的贡献最大,分别为10.1%、12.8%和4.6%;新冠疫情对NO_(2)和PM_(2.5)质量浓度的降低贡献率分别为2.6%和2.0%;周边城市污染源排放措施对降低惠州市各项污染物质量浓度也有一定的贡献,尤其有利于降低NO_(2)和PM_(2.5)的质量浓度(可分别降低1.8%和0.9%)。本研究可为同类城市大气污染防治提供参考。

典型石化企业排放空气质量影响模拟研究

基于惠州市大亚湾区2017年大气污染源排放清单,利用WRF-CMAQ模型系统量化评估了大亚湾区某典型石化企业在关停和增产排放情景下对周边空气质量的影响.清单结果显示该企业2017年SO_(2)、NO_(x)、PM_(10)、PM_(2.5)、CO和VOCs的排放量分别为212、1744、455、359、1458和6446 t,在严格落实等量替代及减排措施后,该石化企业虽然产能翻倍,但VOCs排放量同比2017年显著减少了30%,其它污染物排放量增加了6%~19%.模拟结果显示2017年该石化企业排放对大亚湾区NO_(2)、PM_(10)、PM_(2.5)和O_(3)的浓度贡献分别为0.91、0.64、0.54和-0.08μg·m^(-3),完全关闭该企业排放后对周边站点NO_(2)改善效果最大(可使邻近管委会子站NO_(2)浓度下降1.24μg·m^(-3),下降百分比为5.10%),但由于NO的滴定效应,该企业NO_(x)减排对周边管委会子站和霞涌子站的O_(3)浓度均有轻微负贡献;该石化企业的增产改造对周边O_(3)浓度降低影响明显,周边站点中O_(3)浓度最高可下降2.45μg·m^(-3)(下降幅度为1.72%),大亚湾区O_(3)浓度整体也可下降1.45μg·m^(-3).此外,受秋冬季不利扩散条件以及主导上风向污染传输影响,该企业在1月和10月对管委会子站NO_(2)、PM_(10)和PM_(2.5)的浓度贡献较大,由于冬季低温导致光化学反应自由基活性降低,该企业在1月对管委会子站O_(3)浓度负贡献显著.