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).

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.

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.

Multi-Scale Variation Prediction of PM2.5 Concentration Based on a Monte Carlo Method

Haze concentration prediction,especially PM2.5,has always been a significant focus of air quality research,which is necessary to start a deep study.Aimed at predicting the monthly average concentration of PM2.5 in Beijing,a novel method based on Monte Carlo model is conducted.In order to fully exploit the value of PM2.5 data,we take logarithmic processing of the original PM2.5 data and propose two different scales of the daily concentration and the daily chain development speed of PM2.5 respectively.The results show that these data are both approximately normal distribution.On the basis of the results,a Monte Carlo method can be applied to establish a probability model of normal distribution based on two different variables and random sampling numbers can also be generated by computer.Through a large number of simulation experiments,the average monthly concentration of PM2.5 in Beijing and the general trend of PM2.5 can be obtained.By comparing the errors between the real data and the predicted data,the Monte Carlo method is reliable in predicting the PM2.5 monthly mean concentration in the area.This study also provides a feasible method that may be applied in other studies to predict other pollutants with large scale time series data.

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.

Comparison of Ozone and PM2.5 Concentrations over Urban,Suburban,and Background Sites in China

Surface ozone(O3)and fine particulate matter(PM2.5)are dominant air pollutants in China.Concentrations of these pollutants can show significant differences between urban and nonurban areas.However,such contrast has never been explored on the country level.This study investigates the spatiotemporal characteristics of urban-to-suburban and urban-tobackground difference for O3(Δ[O3])and PM2.5(Δ[PM2.5])concentrations in China using monitoring data from 1171 urban,110 suburban,and 15 background sites built by the China National Environmental Monitoring Center(CNEMC).On the annual mean basis,the urban-to-suburbanΔ[O3]is−3.7 ppbv in Beijing-Tianjin-Hebei,1.0 ppbv in the Yangtze River Delta,−3.5 ppbv in the Pearl River Delta,and−3.8 ppbv in the Sichuan Basin.On the contrary,the urban-to-suburbanΔ[PM2.5]is 15.8,−0.3,3.5 and 2.4μg m^−3 in those areas,respectively.The urban-to-suburban contrast is more significant in winter for bothΔ[O3]andΔ[PM2.5].In eastern China,urban-to-background differences are also moderate during summer,with−5.1 to 6.8 ppbv forΔ[O3]and−0.1 to 22.5μg m^−3 forΔ[PM2.5].However,such contrasts are much larger in winter,with−22.2 to 5.5 ppbv forΔ[O3]and 3.1 to 82.3μg m^−3 forΔ[PM2.5].Since the urban region accounts for only 2%of the whole country’s area,the urban-dominant air quality data from the CNEMC network may overestimate winter[PM2.5]but underestimate winter[O3]over the vast domain of China.The study suggests that the CNEMC monitoring data should be used with caution for evaluating chemical models and assessing ecosystem health,which require more data outside urban areas.

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.

地区空气污染的“压力”与企业绿色转型的“动力”——基于城市PM2.5和公司并购的实证发现

地区空气污染会影响域内各类市场主体行为,包括微观企业决策。碳达峰、碳中和的提出要求各地加强空气污染治理,推动企业绿色转型,而绿色并购是企业绿色转型的重要方式。利用2013—2018年我国A股上市的污染企业发起的并购为样本,并手动搜集整理确定绿色并购样本,实证研究地区空气污染对域内企业绿色转型的影响。研究发现,地区空气污染加剧会提升域内污染企业绿色并购的意愿。在此基础上,通过并购前后企业信息披露质量和社会责任承担变化,研究发现地区空气污染加剧下的企业绿色转型并非源于自主绿色转型发展的内生“动力”,更多是外部“压力”下的“工具主义”行为。从影响路径来看,地区空气污染引致的压力会通过行业竞争和融资约束这两个途径影响污染企业绿色转型的“动力”。最后,异质性检验发现在市场化程度高和非国有企业样本,地区空气污染加剧对企业绿色并购的促进作用更为显著。

融合时空特征的城市多站点PM2.5浓度预测

本文提出一种融合时空特征的城市多站点PM2.5预测方法,该方法可以捕捉PM2.5在时间和空间上的相关性,通过将区域多个站点的PM2.5数据转换为一系列静态图像,将其输入到卷积长短期记忆(ConvLSTM)模型中,采用端对端的方式进行训练,预测城市未来多个站点多个时段的PM2.5浓度。以北京多个站点的PM2.5数据进行实验验证。结果表明:考虑了时空特征的ConvLSTM方法在短期预测方面优于其他4种时序方法,该方法可为PM2.5预测提供新的思路。

基于深度学习的城市PM2.5浓度时空分布预测及不确定性评估

城市PM2.5浓度的时空分布预测旨在基于有限观测样本实现研究区域内PM2.5分布的全范围感知。理想的预测模型需同时保证结果的高精度与高可靠性。然而,现有研究大多以提升精度为唯一目的,忽视了由于数据质量与模型结构的各异所导致预测结果的不确定性,这极大限制了高精度预测结果的可靠性与可用潜力,从而难以有效辅助空气污染治理等实际应用。为此,本文提出一种耦合不确定性评估的PM2.5浓度时空分布预测模型。通过构建以图卷积和循环网络为主的预测模块,实现PM2.5浓度的高精度预测;同时,基于对抗学习策略与变分自编码思想构建不确定性量化模块,同步揭示预测结果的不确定性水平。深圳市实际数据实证表明,本文方法能有效兼顾PM2.5浓度预测结果的精度与可靠性,能为包括监测站点布局选址在内的环境治理工作提供科学决策支持。

二甲双胍通过抑制铁死亡改善PM2.5导致的胎盘滋养细胞功能损伤

目的探究PM2.5损伤胎盘滋养细胞导致不良妊娠结局的作用机制及二甲双胍的挽救作用。方法将16只孕鼠随机分为空白组(n=8),PM2.5染毒组(n=8),两组分别在妊娠1.5、7.5、12.5 d通过气管滴注PBS和PM2.5悬浮液。观察PM2.5对孕鼠妊娠结局的影响,并通过HE染色观察小鼠胎盘病理结构,检测胎盘组织铁死亡相关指标。体外构建PM2.5暴露的人类胎盘滋养细胞系HTR8/SVneo细胞染毒模型,利用CCK8实验检测细胞活性;利用EDU染色检测细胞增殖能力;利用划痕实验检测细胞迁移能力;利用Transwell实验检测细胞侵袭能力;利用成管实验检测细胞管生成能力;通过ELISA和Western blotting等方法分析铁死亡相关指标的表达。结果体内实验显示PM2.5导致胎鼠的质量下降(P<0.001)、数目减少(P<0.01)、死亡率增加(P<0.001),同时胎盘质量也显著减少(P<0.001),HE染色结果显示胎盘的结果受损;组织免疫荧光、ELISA及Western blotting结果表明PM2.5导致胎盘铁死亡的发生。体外实验显示PM2.5通过导致胎盘滋养细胞铁死亡抑制细胞增殖、迁移、侵袭、成管等生物学活性;二甲双胍可显著逆转PM2.5导致的滋养细胞铁死亡,包括细胞内GSH浓度和SOD活性增高(P<0.01)、MDA浓度降低和Fe离子含量降低(P<0.001)、GPX4蛋白和SLC7A11蛋白表达增高(P<0.05)等,同时二甲双胍显著改善PM2.5导致的细胞增殖、迁移、侵袭、成管等生物学功能损伤。结论PM2.5导致孕鼠不良妊娠结局及胎盘滋养细胞铁死亡和功能障碍,应用二甲双胍可有效改善细胞损伤。

中国大气PM2.5污染的动态演进及空间关联格局

文章利用1998—2020年中国370个城市地表PM2.5年度均值面板数据,运用核密度估计图考察大气PM2.5污染的动态演进规律,借助标准差椭圆和探索性空间数据分析方法分别揭示大气PM2.5污染的区位分布和空间关联格局。研究发现:样本期内全国整体与东部、中部、西部和东北四大地区大气PM2.5污染浓度总体均呈波动升高—平稳波动—逐步下降的演变趋势;全国整体与东部、西部地区存在多极分化现象,而中部和东北地区则呈两极分化现象。区位分布主要呈正东—正西走向的空间格局,且在地理空间上总体呈扩散态势;空间集聚模式以“高-高”型和“低-低”型集聚两种模式为主,其中“高-高”型集聚的城市较多且集中在华北、华东北部、环渤海、黄河中游、长江中下游沿岸、新疆南部等地区。

辽宁省秸秆焚烧PM2.5排放量估算及研究分析

进入21世纪以来,辽宁省农民主要农作物是大米、玉米、高粱、油料、谷子、大豆、花生等产生秸秆的农作物。虽然经济水平提高但主要农作物仍然是这些粮食作物,易于产生秸秆。随着农作物单季产量每年不断提高,秸秆总量也在飞速增加,所以多数地区接连出现大规模秸秆焚烧现象;然而,秸秆的大量燃烧不仅造成了丰富而宝贵的生物资源的持续浪费,而且造成了该地区严重的环境污染,主要是在辽宁省的秋收时期,造成区域性重雾霾污染事件发生的重要原因之一就是大面积集中焚烧秸秆。焚烧秸秆产生空气污染物,以PM2.5为主要污染物,易造成雾霾天气。PM2.5因其粒径小、组成复杂、性质多样等特点,具有较强的吸附能力,并且会将产生的可吸入颗粒物、重金属和病毒输送到人体呼吸道和肺部,对人体健康产生很大的负面影响,所以有必要研究这种污染源。辽宁省农作物秸秆理论资源量由2010年的2046.51 × 104 t增长到了2019年的2804.20 × 104 t,增幅37.02%,增幅较大;区域内秸秆资源特别丰富,总焚烧量年均达到近2804.4 × 104 t左右。根据预测的发展趋势,未来秸秆焚烧不会减少,秸秆焚烧产生的PM2.5排放量将逐年增加。但是由于现有的政策以及规范,让秸秆处理更加多样化,使秸秆处理对环境伤害更小,相信在不久的将来农作物秸秆处理会更加完善。

基于专利分析的中国激光PM2.5传感器技术态势研究

为了解当前中国激光PM2.5传感器技术发展状况和发展趋势,使用 HimmPat和Incopat数据库对2010年—2023年国内激光PM2.5传感器技术相关专利进行检索,并从该技术领域的申请态势、专利布局、研发力量、IPC分类和技术功效等方面进行分析。结果显示:1) 数量及趋势:中国激光PM2.5传感器技术专利申请总体态势自2013年进入快速发展期,2017年达到高锋,申请量高达147件。2) 专利布局方面,东部沿海发达地区的专利申请量具有明显数量优势,主要集中在江苏、广东、北京、浙江和湖北,申请人以企业为主,占比80%。3) IPC分类:该领域IPC技术分布较为集中,主要涉及G01D小类(归属借助于测定材料的化学或物理性质来测试或分析材料类别)。4) 技术功效方面,该领域的技术功效重点在于提高精度、提高便利性、降低设备的复杂性、降低成本等,其中,提高精度的专利热点侧重于通过增加检测点,减少误差,提高精度。

赣州市PM2.5质量浓度时空变化特征分析

生态文明建设是中国特色社会主义事业的重要内容,但在城市化进程中人们对经济发展的重视超过了对环境的保护,导致环境污染越来越严重。而大气污染作为环境污染的直观表现,受到了人们的广泛关注。PM_(2.5)作为大气污染源之一,在大气中的含量很少,但它却对人类健康和大气环境造成了极其严重的危害。因此,深入探究PM_(2.5)的时空分布,将会为降低人类暴露强度和健康风险及制订有效的污染防治措施提供重要依据,具有极其重要的意义。

地铁车站PM2.5浓度自注意力混合预测方法研究

建立可靠的空气质量预测模型对经济发展和污染治理至关重要,解决PM2.5浓度的预测问题成为当务之急。本文提出了一种基于自注意力机制的混合预测方法,旨在提高PM2.5浓度的预测精度。使用自注意力机制来捕捉序列中的关键信息;用GRU对序列进行预测;使用DBN对误差序列进行校正,以提高预测的准确性和稳定性,形成了最终的预测序列。为了验证模型的性能,以我国四个地铁车站的室外PM2.5数据为例进行数据处理和预测。结果表明,预测模型在准确性和稳定性方面优于其他参照模型,为决策者提供了科学依据,以更好地治理大气污染问题。

基于HYSPLIT模式的邢台市冬季PM2.5的主要传输途径和潜在贡献源的分析研究

本文利用HYSPLIT模式模拟了2022年11月-2023年1月邢台市100米高度24h后向轨迹,使用了聚类、PSCF、CWT三种不同分析方法分析邢台市冬季PM2.5主要传输途径和潜在贡献源;发现邢台市冬季区域传输的主要传输途径一是来自南、西南方向,包括河南南部、山西长治和邯郸等地;二是源自北、东北方向,包括沧州、德州、保定东部和石家庄、衡水等地,而来自蒙古、甘肃方向的气团有较强的清洗作用。潜在贡献源北方主要分布在保定东部、石家庄、衡水西部等地,南方主要分布在邯郸、安阳、鹤壁等地。

洛阳市PM2.5和O3相关性特征分析

利用洛阳市环境空气质量自动监控系统数据,对洛阳市 2020-2023 年 PM2.5和 O3数据特征统计分析,研究了 PM2.5和 O3的年际、月际变化,探究了 2023 年污染因子、气象条件对 PM2.5和 O3的相关性影响。结果表明,2020-2023 年 PM2.5年均浓度、O3-8h-per 浓度均超过环境空气质量二级标准值,PM2.5和 O3-8h-per 峰值月份分别为 1 月、6 月。PM2.5与 O3在冷季和全年水平上呈负相关性;平均温度低于 20℃时 PM2.5与 O3呈负相关性,高于 20℃时二者呈正相关性;相对湿度低于 75%时,随相对湿度的增加,PM2.5与 O3的负相关性增强;平均风速小于 5.5 m/s 时,随风速的减小,PM2.5与 O3的负相关性增强;气压高于 970 hPa 时,PM2.5与 O3在 0.01 水平上相关性显著,二者呈负相关性。

大气中PM2.5浓度监测及其对健康的影响

随着城市化进程的加速和工业生产的不断发展,大气中的PM2.5浓度逐渐成为人们关注的焦点。本文系统地探讨了PM2.5的来源、生成机制、监测技术、对人体健康的影响以及控制与治理措施。PM2.5主要来源于工业生产、城市交通、农业活动等,实时监测技术、数据采集与处理系统有助于准确掌握PM2.5的浓度。PM2.5对人体呼吸系统、心血管系统和免疫系统均有不良影响,而控制与治理措施则需从政策法规、工业源减排、城市交通和农业活动等多方面进行。综合这些信息,有助于提高公众对PM2.5的认识,促进环境保护和健康保障。