Using Hourly Measurements to Explore the Role of Secondary Inorganic Aerosol in PM_(2.5)during Haze and Fog in Hangzhou, China

This paper explores the role of the secondary inorganic aerosol （SIA） species ammonium,NH4＋,nitrate,NO3-,and sulfate,SO24-,during haze and fog events using hourly mass concentrations of PM2.5 measured at a suburban site in Hangzhou,China.A total of 546 samples were collected between 1 April and 8 May 2012.The samples were analyzed and classified as clear,haze or fog depending on visibility and relative humidity （RH）.The contribution of SIA species to PM2.5 mass increased to ～50％ during haze and fog.The mass contribution of nitrate to PM2.5 increased from 11％ during clear to 20％ during haze episodes.Nitrate mass exceeded sulfate mass during haze,while near equal concentrations were observed during fog episodes.The role of RH on the correlation between concentrations of SIA and visibility was examined,with optimal correlation at 60％-70％ RH.The total acidity during clear,haze and fog periods was 42.38,48.38 and 45.51 nmol m-3,respectively,indicating that sulfate,nitrate and chloride were not neutralized by ammonium during any period.The nitrate to sulfate molar ratio,as a function of the ammonium to sulfate molar ratio,indicated that nitrate formation during fog started at a higher ammonium to sulfate molar ratio compared to clear and haze periods.During haze and fog,the nitrate oxidation ratio increased by a factor of 1.6-1.7,while the sulfur oxidation ratio increased by a factor of 1.2-1.5,indicating that both gaseous NO2 and SO2 were involved in the reduced visibility.

Spatial and Temporal Variation of Particulate Matter (PM10 and PM2.5) and Its Health Effects during the Haze Event in Malaysia

This study aims to assess and compare levels of particulate matter(PM10 and PM2.5)in urban and industrial areas in Malaysia during haze episodes,which typically occur in the south west monsoon season.The high concentrations of atmospheric particles are mainly due to pollution from neighbouring countries.Daily PM concentrations were analysed for urban and industrial areas including Alor Setar,Tasek,Shah Alam,Klang,Bandaraya Melaka,Larkin,Balok Baru,and Kuala Terengganu in 2018 and 2019.The analysis employed spatiotemporal to examine how PM levels were distributed.The data summary revealed that PM levels in all study areas were right-skewed,indicating the occurrence of high particulate events.Significant peaks in PM concentrations during haze events were consistently observed between June and October,encompassing the south west monsoon and inter-monsoon periods.The study on acute respiratory illnesses primarily focused on Selangor.Analysis revealed that Klang had the highest mean number of inpatient cases for acute exacerbation of bronchial asthma(AEBA)and acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with values of 260.500 and 185.170,respectively.Similarly,for outpatient cases of AEBA and AECOPD,Klang had the highest average values of 41.67 and 14.00,respectively.Shah Alam and Sungai Buloh did not show a significant increase in cases during periods of biomass burning.The statistical analysis concluded that higher concentrations of PM were associated with increased hospital admissions,particularly from June to September,as shown in the bar diagram.Haze episodes were associated with more healthcare utilization due to haze-related respiratory illnesses,seen in higher inpatient and outpatient visits(p<0.05).However,seasonal variability had minimal impact on healthcare utilization.These findings offer a comprehensive assessment of PM levels during historic haze episodes,providing valuable insights for authorities to develop policies and guidelines for effective monitoring and mitigation of the negative impacts of haze events.

Characteristic Analysis of OC and EC in PM_(2.5) of Typical Haze Weather in Wuhan City

Long-time large-area haze weather appeared in Wuhan on June 11, 2012. It was monitored that PM2.5 hourly concentration obviously rose, and peak value reached 658 μg/m3. OC and PM2.5 presented high correlation, and correlation coefficient was 0.96. OC daily average concentration occupied 10% -20% in PM2.5, and difference was big between haze and normal weather. EC occupied 5%, and difference was very small between haze and normal weather. By analyzing change trend of OC/EC, it was found that OC/EC presented increasing trend in late May and was even higher than that during 11 -15 June. It was clear that biomass combustion taking straw as the representation has started in late May. But two strong precipitation on May 29 and June 6 inhibited haze weather, and specific meteorological condition caused haze on June 11. Proportion of SOC to OC reached 14% -70%, illustrating that daily difference of secondary photochemical reaction was very big in Wuhan. OC/EC values were respectively 2.7, 3.5 and 4.2 in May, June and haze period. SOC daily means were respectively （5.33 ±4.77） and （32.5 ±23.4） μg/m^3 in May and haze period. Major pollution source of haze weather was biomass combustion, and haze occurrence had very big relationship with special meteorological condition.

Numerical simulation of an extreme haze pollution event over the North China Plain based on initial and boundary condition ensembles

The North China Plain often su ers heavy haze pollution events in the cold season due to the rapid industrial development and urbanization in recent decades.In the winter of 2015,the megacity cluster of Beijing Tianjin Hebei experienced a seven-day extreme haze pollution episode with peak PM2.5(particulate matter(PM)with an aerodynamic diameter≤2.5μm)concentration of 727μg m 3.Considering the in uence of meteorological conditions on pollu-tant evolution,the e ects of varying initial conditions and lateral boundary conditions(LBCs)of the WRF-Chem model on PM2.5 concentration variation were investigated through ensemble methods.A control run(CTRL)and three groups of ensemble experiments(INDE,BDDE,INBDDE)were carried out based on difierent initial conditions and LBCs derived from ERA5 reanalysis data and its 10 ensemble members.The CTRL run reproduced the meteorological conditions and the overall life cycle of the haze event reasonably well,but failed to capture the intense oscillation of the instantaneous PM2.5 concentration.However,the ensemble forecasting showed a considerable advantage to some extent.Compared with the CTRL run,the root-mean-square error(RMSE)of PM2.5 concentration decreased by 4.33%,6.91%,and 8.44%in INDE,BDDE and INBDDE,respectively,and the RMSE decreases of wind direction(5.19%,8.89%and 9.61%)were the dominant reason for the improvement of PM2.5 concentration in the three ensemble experiments.Based on this case,the ensemble scheme seems an e ective method to improve the prediction skill of wind direction and PM2.5 concentration by using the WRF-Chem model.

A new approach to spatial source apportionment of haze pollution in large scale and its application in China

Long-lasting expansion of haze pollution in China has already presented a stern challenge to regional joint prevention and control. There is an urgent need to enlarge and reconstruct the coverage of joint prevention and control of air pollution in key area. Air quality models can identify and quantify the regional contribution of haze pollution and its key components with the help of numerical simulation, but it is difficult to be applied to larger spatial scale due to the complexity of model parameters. The time series analysis can recognize the existence of spatial interaction of haze pollution between cities, but it has not yet been used to further identify the spatial sources of haze pollution in large scale. Using econometric framework of time series analysis, this paper developed a new approach to perform spatial source apportionment. We applied this approach to calculate the contribution from spatial sources of haze pollution in China, using the monitoring data of particulate matter(PM_(2.5)) across 161 Chinese cities. This approach overcame the limitation of numerical simulation that the model complexity increases at excess with the expansion of sample range, and could effectively deal with severe large-scale haze episodes.

Cause Analysis on the Extreme Fog-haze Event in East China in January 2013

Characteristics and cause of the fog-haze event in East China in January 2013 were analyzed with the meteorological conventional observation data and atmospheric composition observation data. Results showed that fog or haze days in east area of Southwest China and most areas of central east China were more than 20 days in this month, especially in east of North China, Huanghuai and Jianghuai regions,that were 10 -15 days more than in the same period of normal years. 500 hPa circulation presented zonal type, cold air wasn＇t active, south-branch trough was weaker, and precipitation amounts was small, which was the weather background of the extreme fog and haze. Northwest airflow at upper layer of East Asia mid-high latitude,less cloud,ground temperature reduction at night, and warm dry air coverd at 850 hPa that led to temperature inversion near surface layer,which was the key thermal factor of fog and haze formation. High humidity near the ground ,weak horizontal wind speed, and weak ascending motion ,which were not favorable for horizontal diffusion and vertical exchange of water vapor and pollutant but were the dynamic factors of maintaining fog and haze. Whether there was a water vapor saturated layer performance in relative humidity /〉90% below 925 hPa or not can be looked as a basis of distinguishing between fog and haze.

A Haze Feature Extraction and Pollution Level Identification Pre-Warning Algorithm

The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.

The Regression Analysis between the Meteorological Synthetic Index Sequence and PM2.5 Concentration

Adapting daily meteorological data provided by China International Exchange Station, and using principal component analysis of meteorological index for dimension reduction comprehensive, the regression analysis model between PM2.5 and comprehensive index is established, by making use of Eviews time series modeling of the comprehensive principal component, finally puts forward opinions and suggestions aim at the regression analysis results of using artificial rainfall to ease haze.

Chemical characteristics of PM_(2.5) during a typical haze episode in Guangzhou

The chemical characteristics（water-soluble ions and carbonaceous species） of PM2.5 in Guangzhou were measured during a typical haze episode.Most of the chemical species in PM2.5 showed significant difference between normal and haze days.The highest contributors to PM2.5 were organic carbon（OC）,nitrate,and sulfate in haze days and were OC,sulfate,and elemental carbon（EC） in normal days.The concentrations of secondary species such as,NO3^-,SO4^2-,and NH4^＋ in haze days were 6.5,3.9,and 5.3 times higher than those in normal days,respectively,while primary species（EC,Ca^2＋,K^＋） show similar increase from normal to haze days by a factor about 2.2-2.4.OC/EC ratio ranged from 2.8 to 6.2 with an average of 4.7 and the estimation on a minimum OC/EC ratio showed that SOC（secondary organic carbon） accounted more than 36.6% for the total organic carbon in haze days.The significantly increase in the secondary species（SOC,NO3^-,SO4^2-,and NH4^＋）,especially in NO3^-,caused the worst air quality in this region.Simultaneously,the result illustrated that the serious air pollution in haze episodes was strongly correlated with the meteorological conditions.During the sampling periods,air pollution and visibility had a good relationship with the air mass transport distance;the shorter air masses transport distance,the worse air quality and visibility in Guangzhou,indicating the strong domination of local sources contributing to haze formation.High concentration of the secondary aerosol in haze episodes was likely due to the higher oxidation rates of sulfur and nitrogen species.

A novel method for distinguishing fog and haze based on PM2.5, visibility, and relative humidity

Haze and fog are both low visibility events, but with different physical properties. Haze is caused by the increase of aerosol loading or the hygroscopic growth of aerosol at high relative humidity, whereas visibility degradation in fog is due to the light scattering of fog droplets, which are transited from aerosols via activation. Based on the difference of physical properties between haze and fog, this study presents a novel method to distinguish haze and fog using real time measurements of PM2.5, visibility, and relative humidity. In this method, a criterion can be developed based on the local historical data of particle number size distributions and aerosol hygroscopicity. Low visibility events can be classified into haze and fog according to this criterion.

Corrosivity of haze constituents to pure Mg

The corrosion behavior of pure Magnesium(Mg)in a Mg(OH)2-saturated solution containing different individual constituents of PM2.5 in haze were studied by hydrogen evolution,weight loss and electrochemical experiments.The results indicated that the corrosivity of these constituents to pure Mg decreased in the following order:(NH4)2SO4>Haze-contaminated-solution>NH4NO3>NH4Cl>NaCl≈KCl≈Na2SO4≈MgCl2≈CaSO4>Mg(OH)2(basic solution)>Ca(NO3)2.Possible mechanisms behind the different corrosion behaviors of Mg in response to these constituents were also briefly discussed in this paper.

PM2.5去除装置设计

设计一款PM2.5去除装置.装置的净化原理是水膜水洗和雾化水净化,PM2.5去除率为70%左右,去除效率较高.

雾霾约束下中国环境技术效率测度及影响因素研究——基于SBM-Undesirable的省际面板数据

将雾霾这一新的约束条件纳入环境技术效率研究框架,基于2001—2012年省际面板数据,运用SBMUndesirable方法测算了雾霾约束条件下的各地区环境技术效率,重点探讨了技术创新与环境规制强度对环境技术效率的影响.效率测算结果显示:雾霾约束下中国整体环境技术效率平均值仅为0.616,环境技术效率水平提升空间较大;环境技术效率区域发展不平衡,东、中、西部地区环境技术效率水平依次下降;各省市环境技术效率差异显著;各地区节能减霾潜力巨大.进一步的效率影响因素分析表明,当前的技术创新、环境规制强度并未能显著改变雾霾污染现状以及提升环境技术效率,经济规模、外资依存度、人口密度及地理位置因素均可以促进环境技术效率提升,产业结构和贸易依存度则有负向作用.

室内PM_(2.5)暴露水平影响研究

近年来,雾霾天气频繁发生,大气污染已引起世界范围的殷切关注;并且成为世界共同研究的课题,其中细颗粒物污染已成为首要问题。而伴随着生活方式的转变,人们越来越注重室内空气品质,因此,室内PM_(2.5)污染已成为亟需解决的问题。通过分析室内PM_(2.5)污染来源以及对人体健康的影响,进行了关于室内PM_(2.5)暴露水平影响的实验研究。结果表明,吸烟是室内PM_(2.5)的主要来源;吸烟时会使室内PM_(2.5)暴露水平显著升高;烹饪会使室内PM_(2.5)暴露水平严重超标;人员活动产生的细颗粒物强度取决于室内的人数、活动类型、活动强度等;室内PM_(2.5)暴露水平受室外影响较大,呈现明显的正相关性。

上海市雾霾污染物对人群健康影响的统计研究

通过收集2013—2015年上海市每日雾霾污染物浓度数据、气象监测数据,并统计研究期间就诊于上海市某医院的门急诊量数据,分析PM 2.5浓度与门急诊量的暴露-反应关系,进而研究PM 2.5对人群健康的影响。采用泊松分布的半参数广义相加模型,以医院每日门急诊量为响应变量,通过样条平滑函数控制长期趋势、季节趋势、星期效应和气象因素等混杂因素,拟合PM 2.5浓度与每日门急诊量的暴露-反应关系模型。结果表明,PM 2.5浓度升高会引起医院呼吸内科、心血管内科和皮肤科门急诊量的增加。

海南地区一次霾过程特征及污染物潜在源区分析研究

利用海南地区常规气象观测数据,结合多种大气污染物浓度数据,针对2014年1月海南地区发生的霾污染事件进行系统分析。结果表明,海南地区北部和东部等多个市县出现持续的霾污染,其中5日最为严重,共有8个市县出现霾,海口市AQI值达到158,PM2.5是此次污染事件的主要贡献者。稳定的大气环流形势提供了持续稳定的大气环境背景,持续的低层东北风有利于污染物从内陆地区向海南输送。后向轨迹表明外源输送与此次污染事件密切相关。

上海的雾霾天气特征及影响因素

对1961至今的上海雾霾日数变化特征及影响因素进行了调研和分析,特别是对2008年以来上海大气环境中粒径2.5μm及以下颗粒物(PM2.5)的年变化、月变化及日变化特征及影响因素做了相关分析,并从PM2.5的角度反映近年来的雾霾天气变化。经分析可知,上海市雾日数整体呈减少趋势,而霾日数呈增加趋势,且增幅明显。上海市PM2.5年平均质量浓度在2008-2014年期间呈降-升-降的趋势,仅2011年的年均值(34μg/m3)达到《环境空气质量标准》(GB3095-2012)中二级标准。另外,上海市PM2.5的月均值冬季高,夏季低,符合霾天气的冬多夏少,春秋季居中的规律,并且其浓度变化与气温和降水量等气象因素有较为明显的相关性。

基于大气雾霾预警的供热锅炉与电厂锅炉负荷调配优化策略

本文首先基于国家环保部和中华人民共和国环境保护中心等单位公开数据,对比分析北方城市空气中PM2.5(大气中直径小于2.5μm的可入肺颗粒物)及PM10(直径小于10μm的可吸入颗粒物)两种颗粒物(PM)的浓度与城市采暖之间联系,经研究表明:3个城市(长春、沈阳、哈尔滨)采暖期PM2.5比非采暖期相比平均提升96.03%,PM10平均提升67.67%,采暖对PM2.5及PM10的空气含量影响很大,尤其是对PM2.5的贡献度更大。因此,要改善城市供暖时期的空气质量,经对比国家电厂锅炉比供热锅炉排放标准,其中烟气颗粒物排放限值下降62.5%,由于电力需求侧不足,本文提出基于大气雾霾预警的供热锅炉与电厂锅炉负荷调配策略,做到既能保护大气环境又能使双方企业受益的策略。

单片机控制的粉尘检测仪设计

设计了一种简易、可行的粉尘检测仪.它由AT89C51单片机和DSM501粉尘传感器组成,以液晶屏显示粉尘浓度,可作为空气中可吸入颗粒物的检测设备使用.

苏州地区灰霾天气污染防治对策的探讨

本文阐述了苏州地区灰霾天气的现状,并结合PM2.5和PM10的监测结果,分析不同季节出现灰霾天气的原因。对造成灰霾天气的颗粒物污染提出了防治措施,同时提出增强灰霾监测能力,建立灰霾监测预警体系并不断完善环境空气质量评价指标体系。