Research on PM_(2.5) Concentration Prediction Algorithm Based on Temporal and Spatial Features

PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollution level.Achieving PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and control.The past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations,making it difficult to further improve the prediction accuracy.However,factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities,leading to the change of PM2.5 concentration in cities.So a PM2.5 directed flow graph is constructed in this paper.Geographic and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between cities.The introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between cities.Based on this,a PM2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper.A spatial feature extraction method based on weight aggregation graph attention network(WGAT)is proposed to extract the spatial correlation features of PM2.5 in the flow graph,and a multi-step PM2.5 prediction method based on attention gate control loop unit(AGRU)is proposed.The PM2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration prediction.Finally,accuracy and validity experiments are conducted on the KnowAir dataset,and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness of the model.

A Hybrid Deep Learning Approach for PM2.5 Concentration Prediction in Smart Environmental Monitoring

Nowadays,air pollution is a big environmental problem in develop-ing countries.In this problem,particulate matter 2.5(PM2.5)in the air is an air pollutant.When its concentration in the air is high in developing countries like Vietnam,it will harm everyone’s health.Accurate prediction of PM2.5 concentrations can help to make the correct decision in protecting the health of the citizen.This study develops a hybrid deep learning approach named PM25-CBL model for PM2.5 concentration prediction in Ho Chi Minh City,Vietnam.Firstly,this study analyzes the effects of variables on PM2.5 concentrations in Air Quality HCMC dataset.Only variables that affect the results will be selected for PM2.5 concentration prediction.Secondly,an efficient PM25-CBL model that integrates a convolutional neural network(CNN)andBidirectionalLongShort-TermMemory(Bi-LSTM)isdeveloped.This model consists of three following modules:CNN,Bi-LSTM,and Fully connected modules.Finally,this study conducts the experiment to compare the performance of our approach and several state-of-the-art deep learning models for time series prediction such as LSTM,Bi-LSTM,the combination of CNN and LSTM(CNN-LSTM),and ARIMA.The empirical results confirm that PM25-CBL model outperforms other methods for Air Quality HCMC dataset in terms of several metrics including Mean Squared Error(MSE),Root Mean Squared Error(RMSE),Mean Absolute Error(MAE),and Mean Absolute Percentage Error(MAPE).

长春市地铁轻轨站内实测PM 2.5,PM 10分析

本文以长春市轨道交通地铁1号线繁荣路站和轻轨(LRT)3号线亚泰大街站内环境为测试站,对站内PM 2.5与PM 10进行了连续11天的数据采样.由Aerocet-831手持式室内颗粒物监测仪测得地铁站内PM 2.5和PM 10浓度区间分别为122.08μg/m^(3)~167.98μg/m^(3)和276.25μg/m^(3)~415.58μg/m^(3),轻轨站内PM 2.5和PM 10浓度区间分别为13.63μg/m^(3)~80.17μg/m^(3)和31.28μg/m^(3)~197.08μg/m^(3),室外环境PM 2.5和PM 10浓度分别为11μg/m^(3)~62μg/m^(3)和23μg/m^(3)~72μg/m^(3).利用Spss统计学软件对数据进行多变量相关性分析,并得知室外PM 2.5浓度与轻轨站内PM 2.5浓度之间的皮尔逊相关性值为0.827,室外PM 10浓度与轻轨站内PM 10浓度之间的皮尔逊相关性值为0.656.

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.

Analysis of PM2.5 concentrations in Heilongjiang Province associated with forest cover and other factors

Atmospheric particulate matter(PM2.5) seriously influences air quality. It is considered one of the main environmental triggers for lung and heart diseases. Air pollutants can be adsorbed by forest. In this study we investigated the effect of forest cover on urban PM2.5 concentrations in 12 cities in Heilongjiang Province,China. The forest cover in each city was constant throughout the study period. The average daily concentration of PM2.5 in 12 cities was below 75 lg/m^3 during the non-heating period but exceeded this level during heating period. Furthermore, there were more moderate pollution days in six cities. This indicated that forests had the ability to reduce the concentration of PM2.5 but the main cause of air pollution was excessive human interference and artificial heating in winter. We classified the 12 cities according to the average PM2.5 concentrations. The relationship between PM2.5 concentrations and forest cover was obtained by integrating forest cover, land area,heated areas and number of vehicles in cities. Finally,considering the complexity of PM2.5 formation and based on the theory of random forestry, we selected six cities and analyzed their meteorological and air pollutant data. The main factors affecting PM2.5 concentrations were PM10,NO_2, CO and SO_2 in air pollutants while meteorological factors were secondary.

A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

Application of Statistical Distribution of PM_(10) Concentration in Air Quality Management in 5 Representative Cities of China

Objective To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard （AQS） and the reduction of particulate matter emission to meet the AQS from the statistical properties （probability density functions） of air pollutant concentration. Methods The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi＇an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the Iognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method. Results The daily PM10 average concentration in the 5 cities was fitted using the Iognormal distribution. The exceeding duration was predicted, and the estimated PMlo emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AO, S. Conclusion Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.

Effects of Trajectory Wind Direction on Ion Concentration of PM_(10)

Objective To study the characterization apportionment of main ion concentrations of PM10 under the influence of trajectory wind direction in London. Methods PM10 samples from 1 May 1995 to 30 October 1995 of Oxford Street of Central London were collected, the metals and anions of which were measured using atomic absorption spectrometry （AAS） and ion chromatography （IC）. Composite trajectories representative of the air mass arriving in London at the same period were calculated based on basic routine back trajectories from the British Atmospheric Data Centre （BADC）. Results Concentration apportionments of main ions were similar when the trajectory was plotted back at 6 h, 12 h, and 24 h, some were obviously different. Mg, Ba, Pb, and Cu had similar peak apportionments at the area 180°-320°, but Zn and Ni at the area of 90°-270°, NO3^- and SO4^2- at the area of 100°-220°. Cl^- concentration peak apportionment was at the area of 220°-300°, which showed that Cl^- mainly came from the North Sea. Conclusion Trajectory wind direction has important effect on ion concentration apportionment of PM10 in London. The ions have similar concentration peak apportionments or their correlation coefficients are statistically significant.

VARIATION CHARACTERISTICS OF THE PLANETARY BOUNDARY LAYER HEIGHT AND ITS RELATIONSHIP WITH PM2.5 CONCENTRATION OVER CHINA

The planetary boundary layer height(PBLH) was calculated using the radiosonde sounding data, including120 L-band operational sites and 8 GPS sites in China. The diurnal and seasonal variations of PBLH were analyzed using radiosonde sounding(OBS-PBLH) and ERA data(ERA-PBLH). Based on comparison and error analyses, we discussed the main error sources in these data. The frequency distributions of PBLH variations under different regimes(the convective boundary layer, the neutral residual layer, and the stable boundary layer) can be well fitted by a Gamma distribution and the shape parameter k and scale parameter s values were obtained for different regions of China. The variation characteristics of PBLH were found in summer under these three regimes for different regions. The relationships between PBLH and PM_(2.5) concentration generally follow a power law under very low or no precipitation conditions in the region of Beijing, Tianjin and Hebei in summer. The results usually deviated from this power distribution only under strong precipitation or high relative humidity conditions because of the effects of hygroscopic growth of aerosols or wet deposition. The OBS-PBLH provided a reasonable spatial distribution relative to ERA-PBLH.This indicates that OBS-PBLH has the potential for identifying the variation of PM_(2.5) concentration.

Analysis on Thermal Environment of Underlying Surface and PM2.5 Concentration in Community Park of Beijing in Winter

Community park is one of the most important landscape spaces for urban people to live outdoors,and people’s perception of environmental microclimate is a direct factor affecting the use frequency and experience of community parks.In this paper,Shijingshan Sculpture Park of Beijing was taken as experimental object.Using the method of fi eld measurement,9-d winter test for 3 months was conducted in three kinds of landscape architecture spaces,including waterfront plaza,open green space and square under the forest.Via regression analysis method,the measured air temperature(Ta),relative humidity of air(RH),particulate matter(PM2.5)were analyzed.It is found that winter sunshine is main infl uence factor of garden microclimate,and there is a negative correlation between local temperature and humidity;local temperature and humidity can regulate the local PM2.5 concentration,and temperature shows negative correlation with PM2.5 concentration,while humidity shows positive correlation with PM2.5 concentration.Meanwhile,via comparative analysis of temperature,humidity and PM2.5 concentration in different types of garden spaces,the infl uence of different space forms,planting forms and materials on thermal environment of underlying surface and PM2.5 concentration was summarized,and design strategy was optimized,to be as benefi cial reference of reconstruction design of community parks.

上海市A城区大气PM_(10)、PM_(2.5)污染与居民日死亡数的相关分析

目的 探讨大气颗粒物污染对人群健康的影响。方法 采用Poisson广义相加模型对上海市A城区大气PM1 0 、PM2 5的日平均污染浓度与居民日死亡数进行相关回归分析 ,并控制了时间长期趋势、气象、季节、一周日效应混杂因素的影响。结果 当大气PM1 0 、PM2 5浓度上升 10 μg m3时 ,总死亡数分别上升 0 5 3%(0 2 2 %～ 0 85 % )、0 85 % (0 32 %～ 1 39% )。结论 大气粗细颗粒物污染具有潜在的急性人群健康危害。

南京市空气中颗粒物PM10、PM2.5污染水平

为了初步调查南京市空气中颗粒物PM10、PM2.5的污染水平,于2001年冬、春、秋3季在南京市的5个典型城市功能区,用大流量采样器收集了50个样品.结果表明,南京市PM10、PM2.5的污染很严重,超标率分别为72%和92%,最大超标倍数达到6.3和9.0,而且对人体健康危害更大的PM2.5占PM10的大部分,约为68%,应引起公众和相关职能部门的高度重视.

我国四城市空气中PM_(2.5)和PM_(10)的污染水平

为研究我国广州、武汉、兰州、重庆４城市空气中ＰＭ２．５和ＰＭ１０的污染水平，在这我国４城市分别设一污染点和对照点进行了为期２年的ＰＭ２．５、ＰＭ１０和ＴＳＰ监测。结果表明，空气中颗粒物的污染是严重的，污染点比对照点更甚．对人体健康危害大的ＰＭ２．５普遍超过美国新标准的２－８倍。

合肥城区PM10及PM2.5季节污染特征及来源解析

于2014年4月、8月、10月和12月在合肥市城区采集了大气PM_(10)和PM_(2.5)样品,对PM_(10)和PM_(2.5)的质量浓度及其化学组分(无机元素、含碳组分和水溶性离子)进行了测定.结果显示:合肥城区的PM_(10)和PM_(2.5)的平均质量浓度高达113,83μg/m3,分别超出国家环境空气质量标准年均PM_(10)和PM_(2.5)限值的1.61和2.37倍.不同粒径的颗粒物中主要化学组分含量的高低顺序基本一致,水溶性离子的含量最高,其次为碳组分,无机元素.利用正交矩阵因子分析(PMF)对合肥城区PM_(10)和PM_(2.5)的本地来源进行解析,结果表明:PM_(10)中二次源、燃煤、机动车尾气尘及地壳尘的贡献百分比分别为32.5%、25.9%、15.7%和25.5%;PM_(2.5)中二次源、燃煤、机动车尾气尘及地壳尘的贡献百分比分别为38.8%、25.9%、9.9%和21.7%.利用激光雷达评估合肥市环境中颗粒物PM_(10)的区域传输,四个季节常规贡献率分别为13.4%、12.9%、13.5%和16.4%.

宁波市环境空气中PM_(10)和PM_(2.5)来源解析

2010年在宁波3个环境受体点采集不同季节的PM10和PM2.5样品,同时采集颗粒物源类样品,分析它们的质量浓度及多种无机元素、水溶性离子和碳等组分的含量.采用OC/EC最小比值法确定了SOC(二次有机碳)对PM10和PM2.5的贡献,据此重新构建了受体化学成分谱.使用化学质量平衡模型对宁波市区的PM10和PM2.5来源进行了解析.结果表明:城市扬尘、煤烟尘、二次硫酸盐和机动车尾气尘是环境空气中PM10的主要来源,其分担率分别为23.0%、15.9%、13.3%和12.3%;对PM2.5有重要贡献的源类是城市扬尘、煤烟尘、二次硫酸盐、机动车尾气尘、二次硝酸盐和SOC,其分担率分别为19.9%、14.4%、16.9%、15.2%、9.78%和8.85%.

重庆主城区大气PM_(10)及PM_(2.5)来源解析

为探讨重庆主城区4个季节大气PM10和PM2.5的主要来源,于2012年2—12月在重庆主城区的工业区、文教区和居住区5个环境监测点同步采集PM10及PM2.5样品,分析了无机元素、水溶性离子、有机碳和元素碳含量及其分布特征.采集了重庆主城区土壤尘、建筑水泥尘、扬尘、移动源(包括机动车、施工机械及船舶)、工业源(包括固定燃烧源及工业工艺过程源)、生物质燃烧源及餐饮源等7类污染源,建立了重庆市本地化的污染源成分谱库.利用CMB(化学质量平衡)受体模型及二重源解析技术分析了PM10及PM2.5的来源.结果表明:重庆主城区大气中ρ(PM10)及ρ(PM2.5)的年均值分别为153.2和113.1μg/m3,超过GB3095—2012《环境空气质量标准》二级标准限值2倍以上.大气PM10的主要来源为扬尘、二次粒子和移动源(贡献率分别为23.9%、23.5%和23.4%),大气PM2.5主要来源于二次粒子和移动源(贡献率分别为30.1%和27.9%).PM10和PM2.5的主要源类贡献率差别不大,表明研究区域内大气颗粒物污染控制应采取多源控制原则.大气PM10来源的季节性变化特征表现为春季和秋季主要以扬尘为主、夏季和冬季主要以二次粒子为主.

北京PM_(2.5)浓度的变化特征及其与PM_(10)、TSP的关系

在连续2年进行累积1周同步采样的基础上,对北京市城区和居住区2个采样点环境空气中PM2.5的浓度及其时间变化特征进行了分析.PM2.5周平均浓度的变化范围为37～346μg/m3,年均浓度接近或超过PM10的二级年均标准.PM2.5浓度具有明显的季节变化特征,即冬季最高,夏季最低.2个采样点PM2.5浓度的周变化与季节变化均相似.PM2.5与PM10、TSP的比值均在冬季最高,春季最低,反映采暖燃烧源对细颗粒物的贡献较大,而沙尘天气对粗颗粒物的贡献较大;其年均值分别为55%和29%.

杭州市大气PM_(2.5)和PM_(10)污染特征及来源解析

2006年在杭州市两个环境受体点位采集不同季节大气中PM2.5和PM10样品,同时采集了多种颗粒物源类样品,分析了其质量浓度和多种化学成分,包括21种无机元素、5种无机水溶性离子以及有机碳和元素碳等,并据此构建了杭州市PM2.5和PM10的源与受体化学成分谱;用化学质量平衡(CMB)受体模型解析其来源。结果表明,杭州市PM2.5和PM10污染较严重,其年均浓度分别为77.5μg/m3和111.0μg/m3;各主要源类对PM2.5的贡献率依次为机动车尾气尘21.6%、硫酸盐18.8%、煤烟尘16.7%、燃油尘10.2%、硝酸盐9.9%、土壤尘8.2%、建筑水泥尘4.0%、海盐粒子1.5%。各主要源类对PM10贡献率依次为土壤尘17.0%、机动车尾气尘16.9%、硫酸盐14.3%、煤烟尘13.9%、硝酸盐粒8.2%、建筑水泥尘8.0%、燃油尘5.5%、海盐粒子3.4%、冶金尘3.2%。

北京地区PM_(10)和PM_(2.5)质量浓度的变化特征

北京市区2003-01-16—04-30PM10和PM2 5的监测结果表明,虽然ρ(PM10),ρ(PM2 5)的变化幅度较大,但是其变化趋势非常相似。PM10,PM2 5质量浓度的日变化呈双峰特征分布。ρ(PM2 5) ρ(PM10)的平均值为56 6%,说明可吸入颗粒物(PM10)中细粒子(PM2 5)的含量大于粗粒子(PM2 5～10)。

天津市大气中PM10、PM2.5及其碳组分污染特征分析

2007年12月-2008年10月期间,分3个时段,设置2个点位,采集了天津市大气环境中PM10和PM2.5样品.用热光反射分析仪测定样品中的碳组分含量,并用OC/EC最小比值法估算二次有机碳（SOC）的浓度.结果表明,市区采样点颗粒物浓度高于郊区,2个采样点的颗粒物浓度变化趋势一致.5月份PM2.5/PM10比值最小,主要由于土壤风沙尘对PM10的贡献较大.PM10和PM2.5中的有机碳（OC）、元素碳（EC）浓度12月份最高,且变化趋势相同.OC占总碳（TC）比例较高,PM10中OC/TC为0.60-0.83,PM2.5中OC/TC为0.55-0.81.碳组分主要集中在PM2.5中,PM10中约有76%的OC存在于PM2.5中.12月份的SOC浓度最高,与12月份的气象条件和污染源排放等因素有关.