The 72 hour backward trajectories in Huludao City from 2019 to 2021 were simulated using the hybrid single-particle Lagrangian integrated trajectory （HYSPLIT） model.Potential Source Contribution Factor Analysis （PSCF） and Concentration Weight Trajectory Analysis （CWT） based on the daily concentration data of PM 2.5 during the same period were used to investigate the potential sources in different seasons and evaluate their contributions to the concentration of PM 2.5 in Huludao City.The results showed that the main potential sources were located in Ulan Buh Desert,followed by southeast Mongolia,eastern Inner Mongolia,Beijing-TianjinHebei region and western Liaoning in winter.In autumn,the main potential sources were southern Liaoning,Beijing-Tianjin-Hebei region,northern Shandong and northern Henan.The relatively high-value sources in spring were sporadically distributed in the Beijing-Tianjin-Hebei region,Shandong and the Bohai Sea,and they were sporadically distributed in the Beijing-Tianjin-Hebei region and northwestern Shandong in summer.

为了解高分辨率地形数据对WRF(weather research and forecasting)模式在兰州地区模拟效果的影响,本论述分别利用模型默认的GTOPO30(Digital Elevation-Global 30 Arc-Second Elevation)地形数据和分辨率为SRTM3(shuttle radar to-pography mission)地形数据对兰州市2019年1月1日～31日的气象场进行了模拟研究,并利用模拟范围内3个气象站的地面观测资料对WRF模式输出的10m风场、2m气温、地面气压和2m相对湿度进行了比较验证.结果表明:(1)10 m风场对地形数据改变比较敏感,采用SRTM3地形数据后,风速在各站点的模拟结果误差均减小,兰州站、榆中站和皋兰站的均方根误差(root mean squared error,RMSE)值相较于默认地形数据模拟结果分别降低了13.08%、21.37%、11.86%,且对风速高值模拟有一定的改善效果,郊区站点对风向的模拟效果优于城市站点;(2)两种方案对2m气温、地面气压和2m相对湿度的WRF模拟结果十分接近,使用SRTM3数据后气温误差略有降低,气压无明显变化,2 m相对湿度在城市站点模拟效果小幅改善.

在力争实现"双碳"目标的大背景下,如何制定合适的减排路径才能较好地实现甘肃省"碳达峰"目标,成为当前亟须考虑的问题.基于 2008-2017年的MEIC清单,考虑不同能源的利用效率及排放特征,对IPAT等式进行本地化修正,计算了 2005-2020年甘肃省的CO2 排放量,并设计 3种情景,研究了不同发展路径下甘肃省CO2 的排放情况.研究发现,2005-2020年甘肃省CO2 排放量呈现波动上升趋势,且煤炭对于CO2 排放的贡献最大、天然气贡献最小,石油和电力的CO2 排放贡献则在逐年升高;甘肃省CO2 排放最大的市(州)依次是兰州、嘉峪关、白银,在现有发展空间布局下嘉峪关、白银、平凉碳减排的潜力更大;在 3种发展情景中,清洁发展情景是较为适合经济欠发达的甘肃省的发展路径,此情景下全省可在2028年左右实现达峰,峰值排放量1.87亿吨,2060年CO2 排放量为峰值的50.5％.

利用HYSPLIT4模式和全球资料同化系统数据,计算了甘肃地区5个站点2017～2018年逐时72h气团后向轨迹;结合各站点颗粒物逐时质量浓度数据,选择颗粒物污染最严重的春冬季,利用轨迹聚类方法分析了甘肃地区后向气流轨迹特征;基于潜在源贡献函数(PSCF)分析法和浓度权重轨迹(CWT)分析法,将各站点分析结果输入TraPSA分析平台进行加权叠加分析,探讨了影响甘肃地区春冬季颗粒物质量浓度的潜在源区及其贡献.结果表明:西北路径是影响甘肃地区的首要路径,其移动速度快、输送距离长、污染程度严重;东北路径次之,主要来源地为蒙古及内蒙古地区;甘肃南部地区受东南路径的短距离输送影响较大,且受到来自青藏高原的输送影响;甘肃地区春季气团轨迹的输送距离较冬季长且输送高度高,冬季PM2.5浓度均值和PM2.5/PM10的比值均较夏季高.多站点PSCF叠加分析发现,PM10潜在贡献源区春季主要分布在新疆东部、准噶尔盆地、塔里木盆地东北部及青海西北部,蒙古南部、四川北部、青海西北部及东部有零星分布;冬季主要位于新疆东部及塔里木盆地、青海西北部及东部、陕西南部;冬季源区整体向南偏移,且省内的短距离输送加强.多站点CWT叠加分析发现,PM10浓度贡献区春季主要位于新疆东部地区、准噶尔盆地附近,蒙古南部及内蒙古北部有线性分布,青海北部及甘肃北部区域有零星分布;冬季主要分布在新疆东部及甘肃北部地区;春季较冬季PM10污染的主要贡献区域更大、污染更重,但省内的短距离传输及颗粒物污染程度减弱.

With the continuous increase in transportation activities, the transportation sector has become an important source of global greenhouse gases. In 2019, road vehicles accounted for nearly three-quarters of the CO2 emissions of the entire transportation sector and will be the key to achieving carbon peaks in the transportation sector. At the same time, air pollutants emitted by road vehicles are also one of the threats to the environment and human health. Based on the long-range energy alternatives planning system (LEAP) model, we constructed the baseline (BAU) scenario, low-carbon (LC) scenario, and enhanced low-carbon (ELC) scenario for the development of the road transport sector in Lanzhou from 2015 to 2040 and simulated energy consumption and emission co-reduction of greenhouse gases and pollutants under policies and measures. The results showed that the energy consumption and CO2 emissions of the LC scenario will peak in 2026, whereas those in the ELC scenario will peak in 2020. In these two scenarios, pollutant emissions such as NOx, CO, HC, PM2.5, and PM10 began to decline sharply between 2015 and 2017, and the downward trend will slow down gradually around 2023. Based on the feasibility of measures and the cost of abatement, the LC scenario can be used as a road vehicle carbon peak scenario in Lanzhou. In this scenario, the reduction rates of energy consumption, CO2, NOx, CO, HC, PM2.5, and PM10 emissions will reach -24.17%, -26.57%, -55.38%, -65.91%, -72.87%, -76.66%, and -77.18% compared with those under the BAU scenario by 2040. At present, the road vehicles in Lanzhou City should focus on structural optimization measures such as clean-energy use of public transportation, electrification of small passenger cars, and phasing out old cars, as well as vigorously promoting low-carbon travel and improving energy efficiency accompanying the development of automotive technology. These efforts will effectively control CO2 and pollutant emissions by road vehicles, and carbon peaks will be achieved as soon as possible. In addition, it is necessary to pay attention to the changes in vehicle types during the implementation of these measures, which most contribute CO2 and various pollutants, in order to make the measures more targeted by changing the number or the market share of new energy of focused vehicle types.

选取兰州市城区4个环境空气质量国控站点2018-2019年的监测数据和兰州市气象站同期的观测资料,分析了兰州市O3浓度的时空分布特征,并探讨了气象因素和相关污染物对ρ(O3-8 h)的影响.结果表明:1)兰州市城区各站点ρ(O3-8 h)的月变化和ρ(O3)小时值的 日变化均呈单峰型,ρ(O3-8h)高值出现在4-8月,ρ(O3)小时峰值出现在15:00左右;2)相关污染物与ρ(O3-8 h)均呈负相关,ρ(O3-8 h)随ρ(NO2)、ρ(CO)、ρ(PM2.5)的增加而降低;3)高温、低湿的环境有利于兰州市城区O3的生成,而特殊的地形条件导致在一定风速下,O3更容易积累;4)分别建立了相关污染物和气象因子的多元线性回归方程,发现在当前气象条件和相关污染物排放现状下,气象因子对兰州市O3的影响比相关污染物的影响更为重要.

利用兰州市5个环境空气质量国控监测站点2019年的监测值,采用统计学方法分析了颗粒物(PM10和PM2.5)质量浓度分布特征与气象因素之间的关系,研究结果表明:(1)兰州市颗粒物(PM10和PM2.5)质量浓度的月变化曲线呈现为"夏低冬高"的变化趋势,主要是冬季逆温频发,混合层高度低,且供暖期间污染物排放量最大;而夏季污染物排放量较低,并且边界层高度高,有利于污染物的扩散.(2)兰州市颗粒物(PM10和PM2.5)质量浓度的日变化呈"午峰晚峰"的双峰型变化趋势,这种变化趋势与生产及人们日常出行高峰的变化有关.(3)颗粒物(PM10和PM2.5)的质量浓度分布与气温、风速呈负相关,与相对湿度正相关.高温大风天气时污染物浓度低,随着气温的升高,风速的增大,大气湍流能力增强,边界层高度抬升,有利于污染物的扩散,污染物浓度降低.