Pollutant Concentration Data Research Articles

Development of a nationally representative set of combined building energy and indoor air quality models for U.S. residences

Americans spend most of their time inside residences where they are exposed to a number of pollutants of both indoor and outdoor origin. Residential buildings also account for ∼20% of the primary energy consumed in the U.S. To provide a tool for future investigations of interactions between energy use and indoor air quality (IAQ) in homes across the U.S. population, we developed a custom set of nationally representative building energy and IAQ mass balance models that predict annual energy use for space conditioning and indoor concentrations of a number of pollutants of both indoor and outdoor origin across the U.S. residential building stock. The residential energy and indoor air quality (REIAQ) model framework is built in Python and integrates between EnergyPlus and a dynamic mass balance model. REIAQ utilizes historical weather data to predict hourly energy consumption, air change rates, and HVAC system runtimes, which are coupled with historical outdoor pollutant concentration data and assumptions for indoor emission sources and other factors to predict hourly indoor pollutant concentrations. Modeled indoor pollutants include PM2.5, UFPs, O3, NO2, and several volatile organic compounds (VOCs) and aldehydes. The REIAQ model set successfully predicted annual space conditioning energy consumption for the U.S. residential building stock within ∼2% of historical data. Modeled indoor concentrations, infiltration factors for outdoor contaminants, and indoor/outdoor ratios of each pollutant all matched closely with observations from prior field studies. Population-weighted annual average indoor pollutant concentrations were also used to estimate the chronic health burden of residential indoor exposures.

Prediction of hourly PM2.5 using a space-time support vector regression model

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Spatial and temporal trends of PM₁₀ and SO₂ in the Richards Bay area

Air pollution is a public health emergency (WHO, 2016a). It is the biggest environmental risk to health with a global responsibility for about 1 in 9 deaths annually. In 2013, Statistics SA indicated that 10 % of all deaths in South Africa were attributed to respiratory diseases. Areas with increased industrial growth such as Richards Bay are particularly vulnerable. The paper analyses the spatial and temporal concentration trends of PM₁₀ and SO₂ in Richards Bay over the last two decades; specifically, since the promulgation of national ambient air quality standards (NAAQS) and minimum emissions standards (MES). Mann-Kendell trend tests was applied to monitoring data from the Richards Bay Clean Air Association (RBCAA) to test for an improving or worsening trend and the significance thereof. The pollution concentration data was also compared to NAAQS and the WHO annual guidelines. Overall, results indicate that although there has been a downward trend in the concentration of PM₁₀ and SO₂ emissions in the study area, the trends are not statistically significant. However, there was indication of statistically significant reducing trends in ambient concentrations at some stations. The concentrations at all stations were below NAAQS.

街道峡谷绿化带设置对空气流场及污染分布的影响模拟研究

PDF HTML阅读 XML下载 导出引用 引用提醒 街道峡谷绿化带设置对空气流场及污染分布的影响模拟研究 DOI: 10.5846/stxb201703080392 作者: 作者单位: 中国科学院生态环境研究中心,重庆市农业科学院生物技术研究中心,中国科学院生态环境研究中心,中国科学院生态环境研究中心 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金重点项目（41430638）；国家重点研究和发展计划项目（2016YFC0503003）；国家自然科学基金青年科学基金项目（31500381）；城市与区域生态国家重点实验室自主重点项目 Simulation study on the influence of green belt settings on air-flow and pollution distribution in street canyon Author: Affiliation: Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,Biotechnology Research Center, Chongqing Academy of Agricultural Science,Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences,Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences Fund Project: The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan) 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:合理设置道路绿化带能够改变街道空气流场进而改善空气质量。利用风洞试验结合数值模型的方法测试了树冠形状和绿化带位置对街道峡谷的风场影响。在数值模型中，采用针对植物模型不同高度赋予不同叶面积密度（leaf area density，LAD）值的新方法近似模拟不同树冠形状的植物，通过输入污染源数据，得到风场及污染物浓度数据结果；与风洞试验结果进行对比，验证了其有效性；进而，利用数值模型分析了不同绿化带设置下街道峡谷内行人的污染暴露特征，结果表明：绿化带位置及树冠形状影响街道峡谷中的涡流结构，形成复杂的细小湍流，从而影响街道峡谷中的风环境和污染物分布。阔叶树冠和位于街道峡谷中央的绿化带位置有利于降低行人污染暴露风险。未来研究中，通过增加风洞试验布点、考虑热力作用及气象因子的影响，或许可以提供更加详细的改善街道峡谷微环境的绿化带布局，为城市大气环境管理和绿地系统建设提供科学依据。 Abstract:Reasonable greenbelt setting can change the air flow field, effectivelyoptimizing air quality. In this study, the effects of crown profiles and location of green belts in street canyon were explored by using wind tunnel and numerical model. In numerical part, an innovative method, assigning different leaf area density (LAD) values to different heights of plant models, was used to simulate the crown profiles in the numerical model, and then the pollution source data was input into the simulation system to calculate the wind field data and pollutant concentration data. After simulate calculation, the numerical model was verified by comparing data from simulation results with the the data obtained from wind tunnel experiment. At last, pollutant exposure characteristics of pedestrians in street canyons under different green belts settings were analyzed using the numerical model. The results show that the differences between green belts lacation and crown profile will affect the vortex structure in the street canyon, resulting in complex turbulence, which affects the wind environment and the pollutant distribution in the street canyon. The deciduous profile and the one central green belt are effective in reducing the pedestrians pollutant exposure. In future study, more monitoring points should be set in the wind tunnel and the influence of thermal and meteorological factors should also be embededinto simulation and wind tunnel experiment, so as to provide a more detailed configuration of the green belt in street planning to alleviate the pollution inthe microenvironment in street canyons, and provide better scientific basis for local atmospheric environment management and the design of urban green space. 参考文献 相似文献 引证文献

Air Quality Based Informational Intervention Framework To Promote Healthy And Active School Travel

This paper presents a comprehensive framework that guides in setting up an effective intervention design to promote active school travel keeping in view the air quality. As lack of physical activities are observed in school going children all over the world so various programs have been launched to encourage active commuting among school going kids using different intervention strategies. So far no effort was found in which the pollutant exposure aspect is taken into account. The primary objective of this study is to develop a computational model that helps in detecting walking/cycling school routes at an individual level that enhance physical activity alongside lesser exposure to air pollutants. The computations involved in this process is dependent on a variety of data sets such as individual’s school travel dairy, street level pollutant concentration data, public and active transport network data. The output generated gives exposure and physical activity involved in the current and suggested school routes. It is taken into consideration that the changes identified are healthy (having low exposure to pollutants) and encourage active school travel. These changes can be considered as alternatives/suggestions to develop the customized information package that can be used as a behavioural intervention tool.

Determination of Possible Effects of Air Pollutants for the Kocaeli-Dilovasi

In the study, it is aimed to determine the possible effects of air pollutants (CO, SO 2 , NO, NO 2 , O 3 ) in the Kocaeli-Dilovasi region where the industry is concentrated with ALOHA software. In the modeling studies, air pollutant concentration data for the years 2010 and 2017 of the National Air Quality Monitoring Network established by the Ministry of Environment and Urban Planning was used. Modeling studies were conducted through direct source and instant release scenarios. Toxic effects were identified for all air pollutants and the largest impact areas were identified for the toxic zone. For NO and NO 2 , the effect distance was determined to be 10km in red (>20 ppm, PAC-3), orange (>12 ppm, PAC-2) and yellow (0.5 ppm, PAC-1) every two years. The worst-case scenario was considered with the selection of the atmospheric F stability class. Toxicity as well as combustion and explosion effects were determined for CO air pollutant. For the year 2010, the flammable region, red (>75000 ppm, 60% LEL) = 2.2 km and yellow (>12500 ppm, 60% LEL) =distance of 4.7km effects were obtained. It was determined that the detonation explosion effects gave larger impact distances (<3km) and showed all explosion effects of the destructions of buildings (>8 psi), serious injury likely (>3.5 psi), and shatters glass (>1.0 psi). It was indicated that concentrations for CO and O 3 air pollutants declined in 2017 and so resulted in a decrease in all impact values. It has been predicted that environmental regulations could be more effective if quantitative assessments of the possible effects of air pollutants were made.

The early-warning system based on hybrid optimization algorithm and fuzzy synthetic evaluation model

Portending and mastering the atmospheric quality plays a significant role in polishing up people's daily life and preventing the environment against serious pollution because air pollution has exceeded the international warning level. Since, accurate contaminants concentration forecasting and objective evaluation, which is urgent to establish an early-warning system, is a critical issue for the social community. An early-warning framework based on two modules composed of forecasting part and assessment part is proposed and successfully adopted in this paper. The forecasting part is applied to a hybrid optimization model with improved Harmony Search Algorithm with PSO strategy to forecast air contaminants concentration, while the assessment part is applied to Fuzzy Synthetic Evaluation Model with entropy weight to evaluate the air quality levels. The proposed early-warning system was investigated in three cities of Jing-Jin-Ji region of China where there exists serious air pollution for the period August 1st 2015 to September 29st 2016. The findings showed that the forecasting model is greatly superior to statistical model and other models on the urban pollutant concentration data. According to the results of air quality assessment, the evaluation model based on the entropy technique can objectively assess the atmospheric quality level.

Effect of Water Quality Sampling Approaches on Nitrate Load Predictions of a Prominent Regression-Based Model

High frequency in-situ measurements of nitrate can greatly reduce uncertainty in nitrate flux estimates. Water quality databases maintained by various federal and state agencies often consist of pollutant concentration data obtained from periodic grab samples collected from gauged reaches of a stream. Regression models, such as the LOAD ESTimator (LOADEST), are frequently used to model variations in concentrations associated with changes in water discharge to provide integrated solute flux measurements. However, uncertainty in the relationships between nutrient concentration and flow may lead to errors in the corresponding flux estimates. In this study, a high frequency, in-situ measurement of nitrate concentration was implemented to ascertain uncertainty in the concentration/discharge relationship caused by nitrate hysteresis. It was found that observed nitrate hysteresis, as influenced by complex storm/watershed interactions, was not readily predictable. Therefore, it can lead to substantial nitrate flux uncertainty, based on periodic grab sample monitoring approaches. Scientists and engineers should take advantage of the proposed findings in future studies to enhance the quality of the associated decision making processes.

Environmental factors can influence dengue reported cases.

Global climate changes directly affect the natural environment and contribute to an increase in the transmission of diseases by vectors. Among these diseases, dengue is at the top of the list. The aim of our study was to understand the consequences of temporal variability of air temperature in the occurrence of dengue in an area comprising seven municipalities of the Greater São Paulo. Characterization of a temporal trend of the disease in the region between 2010 and 2013 was performed through analysis of the notified number of dengue cases over this period. Our analysis was complemented with meteorological (temperature) and pollutant concentration data (PM10). We observed that the months of January, February, March, April and May (from 2010 to 2013) were the ones with the highest number of notified cases. We also found that there is a statistical association of moisture and PM10 with the reported cases of dengue. Although the temperature does not statistically display an association with recorded cases of dengue, we were able to verify that temperature peaks coincide with dengue outbreak peaks. Future studies on environmental pollution and its influence on the development of Aedes aegypti mosquito during all stages of its life cycle, and the definition of strategies for better monitoring, including campaigns and surveillance, would be compelling.

Assessment of Urban Air Pollution and Spatial Spillover Effects in China: Cases of 113 Key Environmental Protection Cities

Abstract: With rapid urbanization and energy consumption, environmental pollution and degradation have become increasingly serious problems in China. At the beginning of 2013, China implemented new ambient air quality standards (GB 3095-2012) in which the concentration of six pollutants including PM2.5, ozone, carbon monoxide, PM10, sulfur dioxide and nitrogen dioxide were monitored. This study gathered annual air pollutant concentration data for the six pollutants in 113 key environmental protection cites throughout China in 2014 and 2015 to explain spatial patterns of urban air pollution. Based on the Kernel density estimation method, spatial hotspots of air pollution were illustrated through which spatial cluster of each pollutants could be plotted. By employing an entropy evaluation system, urban air quality was assessed in terms of the six atmospheric pollutants. We conclude that, in general, CO and SO2 were two important pollutants in most Chinese cities, but this varied greatly among cities. The as...

PM10 Concentration Forecasting Model Based on Wavelet-SVM

PM10 is the main air pollutant in Taiyuan, as the city is a heavy industrial center with coal as its main energy source. Therefore, research on the prediction of this pollutant's variation and concentration is of great theoretical significance for air pollution prevention and emergency solutions. The source of PM10 is very complex, as it is affected by industrial emissions, vehicle exhaust, fugitive dust, and many other factors. The emission sources of PM10 are difficult to determine accurately. The goal of our research was to give accurate forecasting results efficiently when only time-series PM10 concentrations, and no other exogenous information, is available. A support vector machine (SVM) enjoys good generalization performance in the PM10 concentration forecasting area. Traditionally, an SVM chooses historical data as the input features in the process of dealing with the time-series data of air pollutant concentrations. However, data with simple structure and incomplete information have become the fetter of generalization ability improvement. In this study, the data for simulation experiments was the PM10 concentration dataset collected from four monitoring stations in Taiyuan. The PM10 concentration time-series one-dimension data was decomposed into high dimension, constructed by low frequency and high frequency series using a wavelet transform. The wavelet-SVM forecasting model can be established by introducing the high-dimension data as the input features. The experiment results indicate that, contrasted with the traditional SVM, the wavelet-SVM model boasts higher accuracy for PM10 concentration prediction. In particular, it captures the concentration mutational points more accurately and provides information support that is more effective for atmospheric pollution warning. In addition, with the wavelet-SVM model, prediction accuracy for the concentration variations was significantly improved and laws that were more inherent in the PM10 concentration time series were revealed.

A linear and nonlinear approach for interpretation of groundwater quality based on water quality index

In this study using linear and nonlinear approach, aggregate indices for various water quality parameters were analyzed. Using a linear and nonlinear interpolating surface, various water quality variables at grid points were investigated that is essential for mapping. The values help in constituting a key approach for determining the water quality index (WQI), which converts pollutant concentration data into subindex values and then combines them into a single score. One of the biggest advantages of WQI is its summary and intuitive communication capability which will be extremely helpful for demarcating safe aquifer zones of groundwater and in the selection of suitable method for remedial. The present study also describes pollution potential in ground water of Madhya Pradesh, India. The aggregate indices suggest intermediate to high pollution susceptibility in some region of Madhya Pradesh that may increase further if not managed. The overall interpretation of the study will be helpful in formulating suitable approach for water quality interpretation and sustainable planning of groundwater resources.

Exposure to particulate air pollution during early pregnancy is associated with placental DNA methylation

Maternal exposure to particulate matter with aerodynamic diameter <10μm (PM10) during pregnancy results in adverse birth outcomes. Changes in placental DNA methylation might mediate those adverse effects. In this study, we examined the associations between prenatal PM10 exposure and DNA methylation of LINE1, HSD11B2 and NR3C1 in human placenta. One hundred and eighty-one mother newborn pairs (80 fetal growth restriction newborns, 101 normal newborns) participated in this study. The average PM10 exposure of each trimester and of the whole pregnancy was calculated using daily air pollution concentration data. Placental DNA methylation was measured by quantitative polymerase chain reaction-pyrosequencing. Placental LINE-1 DNA methylation was reversely associated with first trimester PM10 exposure 1.78% (−β=1.78, 95% CI: −3.35, −0.22%), while placental HSD11B2 DNA methylation was associated with both first and second trimester PM10 exposure, and relatively increased by 1.03% (95% CI: 0.07, 1.98%) and 2.33% (95% CI: 0.69, 3.76%) for each 10μg/m3 increase in exposure to PM10. Those associations were much more evident in fetal growth restriction newborns than those in normal newborns. In summary, early pregnancy PM10 exposure was associated with placental DNA methylation of LINE1 and HSD11B2, suggesting that such methylation alterations might mediate PM-induced reproductive and developmental toxicity.

Air Quality and Control Measures Evaluation during the 2014 Youth Olympic Games in Nanjing and its Surrounding Cities

Air pollution had become a vital concern for the 2014 Youth Olympic Games in Nanjing. In order to control air pollutant emissions and ensure better air quality during the Games, the Nanjing municipal government took a series of aggressive control measures to reduce pollutant emissions in Nanjing and its surrounding cities during the Youth Olympic Games. The Air Quality Index (AQI) is an index of air quality which is used to inform the public about levels of air pollution and associated health risks. In this study, we use the AQI and air pollutant concentrations data to evaluate the effectiveness of the implementation of control measures. The results suggest that the emission reduction measures significantly improved air quality in Nanjing. In August 2014, the mean concentrations of PM2.5, PM10, SO2, NO2, CO and O3 were 42.44 μg·m−3, 59.01 μg·m−3, 11.12 μg·m−3, 31.09 μg·m−3, 0.76 mg·m−3 and 38.39 μg·m−3, respectively, and fell by 35.92%, 36.75%, 20.40%, 15.05%, 8.54% and 47.15%, respectively, compared to the prophase mean before the emission reduction. After the emission reduction, the mean concentrations of PM2.5, PM10, SO2, NO2, and O3 increased by 20.81%, 41.84%, 22.84%, 21.16% and 60.93%, respectively, which is due to the cancellation of temporary atmospheric pollution control measures. The air pollutants diurnal variation curve during the emission reduction was lower than the other two periods, except for CO. In addition, the AQI of Nanjing and its surrounding cities showed a downward trend, compared with July 2014. The most of effective method to control air pollution is to implement the measures of regional cooperation and joint defense and control, and reduce local emissions during the polluted period, such as airborne dust, coal-burning, vehicle emissions, mobile sources and industrial production.

Estimation of non-point source pollution loads with flux method in Danjiangkou Reservoir area, China

The estimation of non-point source pollution loads into the Danjiangkou Reservoir is highly significant to environmental protection in the watershed. In order to overcome the drawbacks of traditional watershed numerical models, a base flow separation method was established coupled with a digital filtering method and a flux method. The digital filtering method has been used to separate the base flows of the Hanjiang, Tianhe, Duhe, Danjiang, Laoguan, and Qihe rivers. Based on daily discharge, base flow, and pollutant concentration data, the flux method was used to calculate the point source pollution load and non-point source pollution load. The results show that: (1) In the year 2013, the total inflow of the six rivers mentioned above accounted for 95.9% of the total inflow to the Danjiangkou Reservoir. The total pollution loads of chemical oxygen demand (CODMn) and total phosphorus (TP) from the six rivers were 58.20 × 103 t and 1.863 × 103 t, respectively, and the non-point source pollution loads were 39.82 × 103 t and 1.544 × 103 t, respectively, indicating that the non-point source pollution is a major factor (with a contribution rate of 68.4% for CODMn and 82.9% for TP). (2) The Hanjiang River is the most significant contributor of pollution loads to the Danjiangkou Reservoir, and its CODMn and TP contribution rates reached 79.3% and 83.2%, respectively. The Duhe River took the second place. (3) Non-point source pollution mainly occurred in the wet season in 2013, accounting for 80.8% and 90.9% of the total pollution loads of CODMn and TP, respectively. It is concluded that the emphasis of pollution control should be placed on non-point source pollution.

Urban air pollution and meteorological factors affect emergency department visits of elderly patients with chronic obstructive pulmonary disease in Taiwan

Both air pollution and meteorological factors in metropolitan areas increased emergency department (ED) visits from people with chronic obstructive pulmonary disease (COPD). Few studies investigated the associations between air pollution, meteorological factors, and COPD-related health disorders in Asian countries. This study aimed to investigate the relationship between the environmental factors and COPD-associated ED visits of susceptible elderly population in the largest Taiwanese metropolitan area (Taipei area, including Taipei city and New Taipei city) between 2000 and 2013. Data of air pollutant concentrations (PM10, PM2.5, O3, SO2, NO2 and CO), meteorological factors (daily temperature, relative humidity and air pressure), and daily COPD-associated ED visits were collected from Taiwan Environmental Protection Administration air monitoring stations, Central Weather Bureau stations, and the Taiwan National Health Insurance database in Taipei area. We used a case-crossover study design and conditional logistic regression models with odds ratios (ORs), and 95% confidence intervals (CIs) for evaluating the associations between the environmental factors and COPD-associated ED visits. Analyses showed that PM2.5, O3, and SO2 had significantly greater lag effects (the lag was 4 days for PM2.5, and 5 days for O3 and SO2) on COPD-associated ED visits of the elderly population (65–79 years old). In warmer days, a significantly greater effect on elderly COPD-associated ED visits was estimated for PM2.5 with coexistence of O3. Additionally, either O3 or SO2 combined with other air pollutants increased the risk of elderly COPD-associated ED visits in the days of high relative humidity and air pressure difference, respectively. This study showed that joint effect of urban air pollution and meteorological factors contributed to the COPD-associated ED visits of the susceptible elderly population in the largest metropolitan area in Taiwan. Government authorities should review existing air pollution policies, and strengthen health education propaganda to ensure the health of the susceptible elderly population.

The concentration distribution of exposures to particulate air pollution on different road sections

Abstract Urban particulate matter exposure from road transport has already become a great issue in major cities. To study population exposure under different traffic conditions, number concentrations of particulate matter were measured at the center of road lanes, non-motorized lanes, pavement for pedestrians and residential area, respectively. In this paper, different categories of urban roads were chosen according to the traffic characteristics and road layout. The pollutant concentration data were collected by FLUKE 985 airborne particle counter on seven weekdays in August 2015, and the corresponding traffic volume, wind speed and direction, weather condition were also collected. There were no big differences between different types of urban roads for the number concentration, while the mass concentration generally increases with the level of urban roads. The number concentration decreases gradually from the center of urban roads to residential area. The results can be applied to determine the population exposure.

Numerical Simulation to Air Pollution Emission Control near an Industrial Zone

A rapid industrial development causes several environment pollution problems. One of the main problems is air pollution, which affects human health and the environment. The consideration of an air pollutant has to focus on a polluted source. An industrial factory is an important reason that releases the air pollutant into the atmosphere. Thus a mathematical model, an atmospheric diffusion model, is used to estimate air quality that can be used to describe the sulfur dioxide dispersion. In this research, numerical simulations to air pollution measurement near industrial zone are proposed. The air pollution control strategies are simulated to achieve desired pollutant concentration levels. The monitoring points are installed to detect the air pollution concentration data. The numerical experiment of air pollution consisted of different situations such as normal and controlled emissions. The air pollutant concentration is approximated by using an explicit finite difference technique. The solutions of calculated air pollutant concentration in each controlled and uncontrolled point source at the monitoring points are compared. The air pollutant concentration levels for each monitoring point are controlled to be at or below the national air quality standard near industrial zone index.

Assessment of the Krak\xf3w inhabitants\u2019 health risk caused by the exposure to inhalation of outdoor air contaminants

Health risk associated with the exposure to the polluted atmospheric air inhalation was estimated for the residents of Kraków, Poland. The air pollution concentration data were obtained from the air-quality monitoring system of the city in 2007–2016. The carcinogenic risk of the studied subpopulations was not acceptable under the formula of C6H6 > BaP > As(PM10) > Cd(PM10) > Pb(PM10) > Ni(PM10). The total carcinogenic risk (Rt) amounted to 3.04E−04 for children, 2.22E−04 for infants, 1.45E−04 for women, and 1.22E−04 for men. The same risk was calculated for the top three locations of the monitoring stations in this respect, within the city of Kraków: Kurdwanów Housing Estate, Nowa Huta district, and Krasińskiego Av. Non-carcinogenic risk in the case of all six monitoring stations and in respect of all the studied subpopulations, resulting from the exposure to PM10 and for NO2 for all stations in case of children and infants, as well as, for adults at Krasińskiego Av. and Dietla Str. stations was rated medium. For C6H6 in the case of adults, children, and infants the risk was rated low. The total risk (HI) of non-carcinogenic pollution was rated medium and ranged as follows: 6.53 for children, 4.70 for infants, 3.19 for women, and 2.67 for men. That type of risk was decreasing at the station locations as follows: Krasińskiego Av. > Dietla Str. > Nowa Huta district > Kurdwanów Housing Estate > Złoty Róg Str. > Piastów Housing Estate.

Relevance analysis and short-term prediction of PM2.5 concentrations in Beijing based on multi-source data

The PM2.5 problem is proving to be a major public crisis and is of great public-concern requiring an urgent response. Information about, and prediction of PM2.5 from the perspective of atmospheric dynamic theory is still limited due to the complexity of the formation and development of PM2.5. In this paper, we attempted to realize the relevance analysis and short-term prediction of PM2.5 concentrations in Beijing, China, using multi-source data mining. A correlation analysis model of PM2.5 to physical data (meteorological data, including regional average rainfall, daily mean temperature, average relative humidity, average wind speed, maximum wind speed, and other pollutant concentration data, including CO, NO2, SO2, PM10) and social media data (microblog data) was proposed, based on the Multivariate Statistical Analysis method. The study found that during these factors, the value of average wind speed, the concentrations of CO, NO2, PM10, and the daily number of microblog entries with key words ‘Beijing; Air pollution’ show high mathematical correlation with PM2.5 concentrations. The correlation analysis was further studied based on a big data's machine learning model- Back Propagation Neural Network (hereinafter referred to as BPNN) model. It was found that the BPNN method performs better in correlation mining. Finally, an Autoregressive Integrated Moving Average (hereinafter referred to as ARIMA) Time Series model was applied in this paper to explore the prediction of PM2.5 in the short-term time series. The predicted results were in good agreement with the observed data. This study is useful for helping realize real-time monitoring, analysis and pre-warning of PM2.5 and it also helps to broaden the application of big data and the multi-source data mining methods.