Air Quality Estimation Using Nonhomogeneous Markov Chains: A Case Study Comparing Two Rules Applied to Mexico City Data

A nonhomogeneous Markov chain is applied to the study of the air quality classification in Mexico City when the so-called criterion pollutants are used. We consider the indices associated with air quality using two regulations where different ways of classification are taken into account. Parameters of the model are the initial and transition probabilities of the chain. They are estimated under the Bayesian point of view through samples generated directly from the corresponding posterior distributions. Using the estimated parameters, the probability of having an air quality index in a given hour of the day is obtained.

Influence of Dust Weather on Air Quality in Lanzhou City

[Objective] The study aimed to discuss the effects of dust weather on air quality in Lanzhou City. [Met]hod] Under the influence of a strong sandstorm during March 8 -15, 2013 on air quality in Gansu Province, the changes of wind speed, atmospheric visibility and PM10 concentra- tion as well as air pollution in Lanzhou were analyzed. [ Result] The dust weather during March 8 -15, 2013 affected Lanzhou City for the longest time in recent 10 years, and the city suffered medium pollution on March 8 and heavy pollution during March 9 -15 under the influence of the dust weather, while PM10 was the pdmary pollutant. [ Condusion] The research could provide the corresponding technical support for the prevention and control of environmental pollution in Lanzhou City.

Analysis on the Characteristics of Environmental Kuznets Curve and Assessment of Air Quality in Qingdao City

[Objective] The aim was to analyze the characteristics of environmental Kuznets curve (EKC) and assess air quality in Qingdao City. [Method] The EKC characteristics of Qingdao from 1996 to 2008 were studied based on air pollutant discharge indices, air pollution indices and economic data from the environmental quality reports and statistics yearbooks of Qingdao, and the air quality of Qingdao City was assessed. [Result] With the increase of GDP per capita, the total discharge of industrial waste gases improved remarkably, and its EKC showed inverted U type. Meanwhile, the discharge of SO2, industrial fumes and industrial dusts decreased on the whole, and the EKC of sulfur dioxide and industrial fumes was close to inverted N type. In addition, air pollution composite index also showed decrease trend, and its EKC showed inverted N type. In recent 13 years, the concentration of SO2, particles and NO2 in ambient air of Qingdao City showed decrease trend on the whole, especially particles. In addition, PM10 has replaced SO2 as the primary pollutant since 2001, and air pollution type has changed from coal smoke pollution to mixed mode pollution of coal smoke and motor vehicle. The mitigation of air pollution in Qingdao City could be attributed to the implementation of industrial waste gas management by the governments of Qingdao City. [Conclusion] The study could provide theoretical foundations for the establishment of environmental policies in Qingdao City.

Climatology of Air Quality in Arctic Cities—Inventory and Assessment

Freely available data of sulfur dioxide (SO2), ammonia (NH3), nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM) observed in Arctic cities (north of 59.99 N) between 1972 and 2016 were compiled into an air-quality inventory of samples taken for limited periods. For cities with multiple years of data, air-quality climatology was determined in terms of daily means in the annual course. Mean urban air-quality climatology was calculated for regions of similar insolation, emission standards, topography, K&#246ppen-Geiger classification, and city size. Urban concentrations of PM precursors (SO2, NH3, NO2), PM2.5 and PM10 (PM with diameter less than 2.5 and 10 μm) were assessed in the sense of climatology with evidence from current knowledge. Typically, annual SO2 and NO2 means were lower for small than large Arctic cities, but can vary more than an order of magnitude over short distance. Cities seeing seasonal sea-ice had W-shaped mean annual courses of daily O3, while other cities had a spring maximum. Typically, annual means of urban pollutants in North America exceeded those in Scandinavia except for O3, where the opposite was true. Annual mean urban PM2.5 and PM10 concentrations varied from 1.6 to 21.2 μg·m-3 and 2 to 18.2 μg·m-3, respectively. Since PM10 encompasses PM2.5, annual PM10 means must be at least 21.2 μg·m-3. According to rural-to-urban ratios of species, seasonal transport of pollutants from wildfires, shipping, and the Kola Peninsula mining area occurred at some sites in Interior Alaska, western and northern Norway, respectively. Concurrent SO2 and PM or NO2 and PM measurements revealed combustion or traffic as major contributors to urban concentrations. Recommendations for potential future measurements of Arctic urban air quality were given based on the assessments of climatology and inventory.

Fuzzy Comprehensive Evaluation and Prediction of Air Quality in Baoding City

Nowadays,the quality of the air environment is closely related to the health of the ecosystem and the safety of people living. With more and more attention attached to the quality of the air environment,it is imminent to analyze the future development trend of air quality. In this paper,the grey correlation analysis was used to determine the weights of 6 pollution indexes,namely PM10,PM2. 5,SO2,NO2,CO and O3. The fuzzy comprehensive assessment method was applied to determine the air quality eigenvalues H（ 2. 145,1. 926,and 1. 805） of Baoding City in 2014-2016,suggesting that the air quality in Baoding is getting better and better. In addition,the grey forecasting model GM（ 1,1） was used to forecast and test the air quality of Baoding City. The results show that the prediction is good.

Air Quality, Health and Community Action

Air quality is impacting health in many cities in most countries because of particulate pollution, sulfur dioxide, nitrogen dioxide, and ozone. Very small particulates from engine emissions and coal fired electric power plants enter the lungs and pollute the blood of urban residents leading to a burden of disease with more than 3 million premature deaths per year attributed to outdoor air pollution. Welfare losses including premature deaths associated with air pollution were about $5 trillion in 2013. A global transition to electric vehicles, and the generation of electricity without combustion emissions would improve air quality significantly and reduce greenhouse gas emissions. This transition is in progress in many parts of the world with more than 2 million electric vehicles in service in 2017. Electric bus and electric taxi sales are increasing, and many large cities have multiple programs to improve air quality. When health costs are considered, it is very appropriate for communities to take action to improve air quality and health. This work reviews and reports many positive actions that are in progress in larger cities.

Comprehensive Review on Air Pollution Control Measures for Non-Attainment Cities of Uttar Pradesh, India

Introduction: The Indian state of Uttar Pradesh (UP) for the past many years has been reported to have many cities with highly polluted air quality. The state has been taking meticulous steps in combating air pollution in the form of action plans, introduced especially in its 17 non-attainment cities (NAC). To assess the progress and development of these action plans in UP, the present study has done an in-depth analysis and review of the state’s action plans and city micro action plans. Materials and Methods: In this research study, the analysis of the latest action plan reports, micro action plan reports as well as the recommendations for combating air pollution-related issues in the 17 NAC of the UP state has been well documented. Uttar Pradesh Pollution Control Board (UPPCB) has prepared these reports to highlight the progress of the plans in response to the growing air pollution in these cities. The information present in the reports has been used to further study sector-specific, category-specific action plans, institutional responsibility, and the present status of the action plans. Results: On average, the highest weightage in action plans was given to sector-specific categories such as Road dust and construction activities (24%). It was also observed that Urban local bodies (~50%) were majorly responsible to implement the action points and 56% of the action points were jointly implemented by multiple agencies.

Characteristics and Cause Analysis of Heavy Haze in Changchun City in Northeast China

Northeast China has been reported as having serious air pollution in China with increasing occurrences of severe haze epi- sodes. Changchun City, as the center of Northeast China, has longstanding industry and is an important agricultural base. Additionally, Changchun City has a long winter requiring heating of buildings emitting pollution into the air. These factors contribute to the complex- ity of haze pollution in this area. In order to analyze the causes of heavy haze, surface air quality has been monitored from 2013 to 2015. By using satellite and meteorological data, atmospheric pollution status, spatio-temporal variations and formation have been analyzed. Results indicated that the air quality in 88.9% of days exceeding air quality index （AQI） level-1 standard （AQI 〉50） according to the National Ambient Air Quality Standard （NAAQS） of China. Conversely, 33.7% of the days showed a higher level with AQI 〉 100. Ex- treme haze events （AQI 〉 300） occurred frequently during agricultural harvesting period （from October 10 to November 10）, intensive winter heating period （from Late-December to February） and period of spring windblown dust （April and May）. Most daily concentra- tions of gaseous pollutants, i.e., NO2 （43.8 gg/m3）, CO （0.9 mg/m3）, SO2 （37.9 gg/m3）, and 03 （74.9 gg/m3） were evaluated within level-1 concentration limits of NAAQS standards. However, particulate matter （PM2.5 and PMI0） concentrations （67.3 ~tg/m3and 115.2 ~g/m3, respectively） were significantly higher than their level-1 limits. Severe haze in spring was caused by offsite transported dust and windblown surface soil. Heavy haze periods during fall and winter were mainly formed by intensive emissions of atmospheric pollutants and steady weather conditions （i.e., low wind speed and inversion layer）. The overlay emissions of widespread straw burning and coal combustion for heating were the dominant factors contributing to haze in autumn, while intensive coal burning during the coldest time was the primary component of total emissions. In addition, general emissions including automobile exhaust, road and construction dust, residential and industrial activities, have significantly increased in recent years, making heavy haze a more frequent occurrence. There- fore, both improved technological strategies and optimized pollution management on a regional scale are necessary to minimize emis- sions in specified seasons in Changchun City, as well as comprehensive control measures in Northeast China.

汉中市生态旅游气候资源特征分析

气候资源合理开发利用是生态文明建设的重要组成部分。通过分析汉中市2015-2022年SO_(2)、NO_(2)、PM_(10)、PM_(2.5)质量浓度,空气质量指数(AQI),环境空气质量优良天数,大气空气质量综合指数以及市域内11个国家气象站气温、降水量、降水日数、日照等气象观测资料,评估汉中市生态旅游气候资源特征,以期为合理规划和深度挖掘汉中市生态旅游气候资源提供科学依据。结果表明:2015-2022年汉中市主要大气污染物质量浓度整体呈逐年下降趋势;大气空气质量综合指数呈逐年下降趋势,2022年达到最低;2015-2022年环境空气质量优良天数均达到280 d以上,呈逐年明显增加趋势;大气空气质量指数月分布呈先减后增的变化趋势,1月份最高,达到中度污染级别,10月份最小,达优秀级别。2015-2022年汉中市年平均气温为14.5℃,极端最高气温为42.0℃,出现在8月份,极端最低气温为-15.6℃,出现在1月份;年均降水量为980.1 mm,降水主要集中在每年的6-10月份,年降水日数集中在每年的4-10月份,年均日照时数为1525.9 h,每年的2-8月份日照时数较多,均在100 h以上,峰值出现在7月份。

Spatio-temporal Differences and Driving Forces of Air Quality in Chinese Cities

Deterioration of city air quality is the serious problem in the process of rapid urbanization and economic development in China. Taking 74 cities that have implemented the new Ambient Air Quality Standards （GB3095- 2012） since 2013 as examples, using statistical and ArcGIS spatial analysis method, the multi-scale temporal and spatial variations characteristics and the impact of social and economic factors on urban air quality variations, are analyzed in this paper. The main research conclusions are as follows：（1） Air quality in Chinese cities shows very significant seasonal variations, with higher air quality in summer and autumn, and lower air quality in spring and winter; （2） Seen from a daily pollution perspective, air pollution is very serious and will tend to be more serious in the future; （3） Seen from an hourly variation perspective, urban air quality is time coupled with social production and urban living; （4） The overall spatial pattern of urban air quality is high in the east and north and low in the west and south, but with an obvious trend towards regional integration. （5） Cities in different regions have different factors that cause air quality variations. In general, urbanization level and energy consumption per GDP are the common factors.

新余市森林游憩区空气负离子与PM_(2.5)动态变化规律及影响因子特征研究

为揭示江西省新余市森林游憩区空气负离子与PM_(2.5)时空分布特征、交互效应及其影响因素,对其森林环境中空气负离子和PM_(2.5)浓度时空分布特征及其与气象因子关系进行测定分析。结果显示:(1)新余市森林游憩区与城市绿地空气负离子年变化趋势总体保持一致,全年中各月份森林空气负离子浓度均显著高于城市绿地;1月、3月和4月城市绿地PM_(2.5)浓度显著高于森林,其他月份差异不明显;夏季6—8月空气负离子和PM_(2.5)浓度分别达到其全年最高值和最低值。(2)森林环境空气负离子浓度随海拔升高呈逐渐上升的趋势,PM_(2.5)含量呈先上升再下降的趋势;随距地面垂直高度的增加,林分中空气负离子浓度先下降再上升后保持不变,PM_(2.5)含量逐渐降低,距地面高10 m处林冠中层具有较高的空气负离子和较低的PM_(2.5)含量。(3)不同季节森林空气负离子与PM_(2.5)浓度均呈显著负相关,与空气气压、温度、相对湿度、风速和风向相关性各不相同。结果表明:新余市森林游憩区夏季、1000 m海拔山地、林冠层中部空气质量最优,是建设森林空气保健、疗养活动的理想场所。

机动车限行对西安市空气质量的影响及其模型分析

近年来,空气污染已成为限制西安城市人居环境质量提升的重要因素之一。当地政府采取了一系列的措施进行空气污染治理,其中机动车限行政策以其可实施性强、公共投入较少、简单易行等特点而最受青睐。在分析2016―2020年间西安市空气质量变化趋势的基础上,运用R语言针对相关影响因子(限行政策、平均风速、相对湿度和平均温度)与空气质量之间的关系建立了广义相加模型,并进行了拟合分析和相关性检验,结果表明机动车限行政策与西安市空气质量改善具有较明显的相关性。

2015-2022年兰州市环境空气质量时间变化特征分析

【目的】分析兰州市环境空气质量时间变化特征,为兰州地区空气污染预报预警和大气污染防治工作提供依据。【方法】利用2015年1月1日—2022年12月31日空气质量逐小时数据,统计分析兰州市空气质量和空气质量等级日数的年际变化、月变化及日变化特征。【结果】结果表明:①2015—2022年兰州市AQI指数呈逐年下降趋势,优良天数呈增加趋势,轻度污染和中度污染天数呈减少趋势;兰州空气质量以优、良和轻度污染为主。②兰州市1—12月AQI指数呈“冬春高夏低”特征,优良天数整体呈先增后降的趋势,轻度污染和中度污染天数呈倒“U”形,12月的污染天数为最多,次峰值则出现在3月。③兰州市逐小时AQI指数呈典型的双峰特征,白天峰值出现在9时至12时,夜间峰值出现在20时至23时。【结论】研究结论可为兰州地区空气污染预报预警和大气污染防治工作提供依据。

城市空气质量指数的时间序列分析和预测--以扬州市为例

城市空气质量指数是反映城市环境宜居水平的重要指标,预测城市空气质量指数可以为政策制定提供重要的参考依据。选取扬州市2014-2022年的空气质量指数(AQI)进行时间序列分析,采用的时间序列模型有自回归积分滑动平均模型(ARIMA)、先知模型(Prophet)以及人工智能领域的长短期记忆模型(LSTM)。通过这些模型分析扬州市空气质量指数的周期性、季节性和趋势特征,并对空气质量指数进行预测。结果显示,基于人工智能的长短期记忆模型具有很强的预测能力。

主成分分析法在合肥市空气质量评估中的应用

基于2019年1月1日至2020年12月31日地面国控站点的重要污染物日均浓度监测数据和气象数据,利用主成分分析法对合肥市城市空气质量进行了综合评估。其中污染物数据包括细颗粒物(PM_(2.5))、可吸入颗粒物(PM_(10))、二氧化氮(NO_(2))、二氧化硫(SO_(2))、一氧化碳(CO)和臭氧(O_(3))等重要大气污染物的日值数据;气象数据包括平均气温、平均风速、日照时数、20-20时累计降水量、平均本站气压和平均相对湿度。首先通过逐步回归法筛选出与空气质量指数有显著影响的指标,再通过主成分分析法对显著指标进行数据降维。根据主成分分析理论,从5个显著影响指标中提取出2个主成分,所提取的两个主成分累积方差贡献率达到82.9%。评估合肥市城区空气质量的第一主成分为PM_(2.5)、CO和PM_(10)组成的综合指标,表明合肥市空气质量受PM_(2.5)、CO和PM_(10)污染物的浓度变化影响最为显著;第二主成分为日照时数和O_(3)组成的综合指标,表明日照时数和O_(3)浓度的变化是影响空气质量的第二重要因素。主成分综合得分与空气质量指数具有较好的一致性(R^(2)=0.78),对比分析发现,主成分分析法对于优良空气质量及冬季的评估效果最好。研究结果表明,运用主成分分析法提取的主成分结合综合得分可对城区空气质量进行有效评估。

城市空气质量的经济价值评估——基于土地市场的视角

结合微观土地交易和城市PM2.5地表年均浓度等数据,研究空气污染对土地价格的影响及传导机制,并通过“两步法”评估中国城市空气质量的经济价值。基准结果显示,城市空气质量改善显著提高了土地价格,并估计出中国城市空气质量的经济价值约为27.369元/m2。通过剔除冬季集中供暖地区样本、改变模型设定形式,以及将核心解释变量滞后一期、两期处理后,基准回归的结论依然稳健。采用城市的空气流通系数和逆温天数作为空气污染的工具变量,内生性处理结果表明空气污染与城市中心地价之间存在负向的因果关系,进一步支持了基准结论。考虑到所处区域和城市层级等方面存在的差异,研究发现中国城市清洁空气的经济价值呈现出异质性。具体而言,东部地区城市空气质量的经济价值明显高于中西部地区,不同层级城市空气质量的经济价值也存在一定的梯度,其中一线城市最高,四线城市最低。机制检验结果表明,城市人口规模是土地价格反映空气质量经济价值的关键渠道。

太原市城区冬季大气污染状况分析

利用2019—2021年冬季(12月、1月和2月)太原市城区9个国家空气质量自动监测站的空气质量指数(Air Quality Index,AQI)和PM_(10)、PM_(2.5)、NO_(2)、SO_(2)、O_(3)、CO质量浓度监测数据,运用统计学方法,分析了太原市城区2019—2021年冬季大气污染状况。总体上,太原市环境污染得到了有效控制,PM_(10)和PM_(2.5)仍为太原市冬季主要污染物。

基于多源城市数据的空气质量预测模型

针对目前空气质量预测研究方法只考虑单一站点时序特性的缺陷问题,提出一种基于多源城市数据的STRGNN模型。将相邻站点之间的空间相关性表示为距离图和相似图,根据领域类别对特征分组;将分组特征与构建的距离图和相似图成对组合输入至模型。通过收集邻近信息捕获空气质量的空间相关性,利用叠加多层的卷积捕获空气质量的时间相关性。仿真结果表明,该模型与GRU、Seq2Seq等4个基准模型相比,在1 h-6 h预测的MAE和RMSE分别降低了3%和1%,预测效果有所提升,验证所提方法与现有方法相比具有优越性。

西宁市夏季居民区空气负离子浓度变化特征及其影响因子分析

利用2022年5-10月在青海省西宁市典型居民区观测的空气正、负离子浓度,分析空气负离子浓度时间分布特征、评价空气清新度和清洁度,结合同期气象条件和空气污染物浓度探究其相关性。结果表明:(1)西宁市典型居民区上午的空气负离子浓度高于下午及晚上,8-10月空气负离子浓度高于5-7月。(2)西宁市居民区空气负离子浓度变化跟监测时的天气状况密切相关,空气负离子浓度变化遵循“雨天>阴天>多云>晴天”的规律。(3)空气负离子浓度高说明空气清新却不代表空气清洁,高原地区城市晴天空气负离子浓度不高,空气清新度一般,而空气清洁度较高。(4)影响西宁市居民区空气负离子浓度波动的主要气象因素为相对湿度、日照时数、水汽压、降水量和风速,抑制西宁市居民区空气负离子浓度升高的主要空气污染物为NO_(2)、PM_(10)、CO和PM_(2.5)。研究结果可为高原地区居住环境、旅游健康管理以及生态环境保护提供科学数据支撑。

南疆最大城市2018―2022年环境空气质量变化特征分析

基于2018—2022年南疆最大城市(库尔勒市)六项大气污染物监测数据(PM_(2.5)、PM_(10)、SO_(2)、NO_(2)、CO和O_(3)),分析了库尔勒市近五年环境空气质量变化特征及其污染物浓度水平.结果表明:2018―2022年库尔勒市空气质量总体逐年向好,空气质量优良天数由2018年的223 d增长到2022年的250 d,增加了27 d;达标率由2018年的62.5%增长到2022年的68.5%,增长了6.0%,重度及以上污染天数降低了23.3%.环境空气质量指数(Air Quality Index,AQI)降低了5.4%,五年的AQI值均高于100,空气质量处于轻度污染水平.常规六项污染物中SO_(2)和CO的浓度呈逐年下降趋势,NO_(2)浓度年际变化不明显,但SO_(2)、NO_(2)、CO的年均值和季节均值均优于国家环境空气污染物浓度限值一级标准.O_(3)的年均值优于国家环境空气污染物浓度限值一级标准,但O_(3)四季的浓度年际变化均呈上升趋势,且2021年和2022年夏季和秋季浓度值均高于100μg·m_(-3).2018—2022年库尔勒市PM_(2.5)呈下降趋势,但PM10变化不明显,PM_(10)各年浓度值均大于国家环境空气污染物浓度限值二级标准(70μg·m^(-3))2倍以上,说明库尔勒市近几年PM_(10)污染非常严重.此外,每年1月PM_(2.5)/PM_(10)最高、3月PM_(2.5)/PM_(10)最低,说明1月至3月粒径大于2.5μm的颗粒物浓度逐渐增加,这主要是因为库尔勒市每年春季受沙尘天气的影响.