Data Analysis on Outdoor–Indoor Air Quality Variation: Buildings’ Producing Dynamic Filter Effects

Recently, air quality issues have attracted much more attention. This paper aims to find an effective way to analyse the buildings’ effects on the air quality variation between indoor and outdoor. To do so, we treat the building as a dynamic filter system by regarding the outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula>, the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula>, and the building as the input, the output, and a response system, respectively. To analyze the filtering effect produced by buildings, the statistical distribution of the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> per hour is studied, and the interrelationship between the indoor and the outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> is explored in time domain. Some interesting physical laws are discovered by using the collected data. First, the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> per hour follows Gaussian distribution in most cases. Second, the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> has a positive correlation with the corresponding outdoor one. Third, a linear regression model with high accuracy on analyzing the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> is presented, which indicates that the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> consists of two parts—one comes from the outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> penetrating into the building and the other comes from the indoor environment. Fourth, by applying different system identification methods, it is found that the B–J model is the best one in characterizing the memory effects of the building for both long time and short time scales. Particularly, for the long time memory effect, the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> average memory duration (AMD) is about 2 h, and the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> AMD to the outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> is about 7 h, while for the short time memory effect, the indoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> AMD is also about 2 h but that to the outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> is about 5 h. Additionally, the continuance of outdoor PM<inline-formula><tex-math notation="LaTeX">$_{2.5}$</tex-math></inline-formula> has much greater effect on the indoor one than its concentration.

[1]  Ke Gu,et al.  Recurrent Air Quality Predictor Based on Meteorology- and Pollution-Related Factors , 2018, IEEE Transactions on Industrial Informatics.

[2]  Michael Brauer,et al.  Addressing Global Mortality from Ambient PM2.5. , 2015, Environmental science & technology.

[3]  You-Chiun Wang,et al.  Efficient Data Gathering and Estimation for Metropolitan Air Quality Monitoring by Using Vehicular Sensor Networks , 2017, IEEE Transactions on Vehicular Technology.

[4]  Gerhard P. Hancke,et al.  Air Quality Monitoring System Based on ISO/IEC/IEEE 21451 Standards , 2016, IEEE Sensors Journal.

[5]  Eleni Fotopoulou,et al.  Linked Data Analytics in Interdisciplinary Studies: The Health Impact of Air Pollution in Urban Areas , 2016, IEEE Access.

[7]  H. R. Anderson,et al.  Epidemiological time series studies of PM2.5 and daily mortality and hospital admissions: a systematic review and meta-analysis , 2014, Thorax.

[8]  Tanvi Banerjee,et al.  Investigation of an Indoor Air Quality Sensor for Asthma Management in Children , 2017, IEEE Sensors Letters.

[9]  J. Schauer,et al.  Source apportionment of PM2.5 in the Southeastern United States using solvent-extractable organic compounds as tracers. , 2002, Environmental science & technology.

[10]  张桦,et al.  BlueAer: A fine-grained urban PM2.5 3D monitoring system using mobile sensing , 2016 .

[11]  Yu Zheng,et al.  p-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data , 2016, ArXiv.

[12]  J. Sunyer,et al.  Sources of indoor and outdoor PM2.5 concentrations in primary schools. , 2014, The Science of the total environment.

[13]  Victor O. K. Li,et al.  A Gaussian Bayesian model to identify spatio-temporal causalities for air pollution based on urban big data , 2016, 2016 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[14]  Ling-Jyh Chen,et al.  An Open Framework for Participatory PM2.5 Monitoring in Smart Cities , 2017, IEEE Access.

[15]  Mihaela Oprea,et al.  A knowledge based approach for PM2.5 air pollution effects analysis , 2016, 2016 International Symposium on INnovations in Intelligent SysTems and Applications (INISTA).

[16]  Ke Gu,et al.  Highly Efficient Picture-Based Prediction of PM2.5 Concentration , 2019, IEEE Transactions on Industrial Electronics.

[17]  Jui-Hung Chang,et al.  Analysis of Correlation Between Secondary PM2.5 and Factory Pollution Sources by Using ANN and the Correlation Coefficient , 2017, IEEE Access.

[18]  K. Shimizu,et al.  Analysis of Hexadecane Decomposition by Atmospheric Microplasma , 2018, IEEE Transactions on Industry Applications.

[19]  Cunlin Zhang,et al.  Evaluating PM2.5 at a Construction Site Using Terahertz Radiation , 2015, IEEE Transactions on Terahertz Science and Technology.

[20]  Michael Brauer,et al.  Ambient PM2.5 Reduces Global and Regional Life Expectancy , 2018, Environmental Science & Technology Letters.

[21]  Marios M. Polycarpou,et al.  Distributed Contaminant Detection and Isolation for Intelligent Buildings , 2018, IEEE Transactions on Control Systems Technology.

[22]  Zhu Han,et al.  Real-Time Profiling of Fine-Grained Air Quality Index Distribution Using UAV Sensing , 2017, IEEE Internet of Things Journal.

[23]  Zhongfei Zhang,et al.  Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality , 2017, IEEE Transactions on Knowledge and Data Engineering.

[24]  N. Probst-Hensch,et al.  Differences in indoor versus outdoor concentrations of ultrafine particles, PM2.5, PMabsorbance and NO2 in Swiss homes , 2015, Journal of Exposure Science and Environmental Epidemiology.

[25]  Qiang Li,et al.  Meteorological conditions for the persistent severe fog and haze event over eastern China in January 2013 , 2013, Science China Earth Sciences.

[26]  Weihong Han,et al.  A New Air Pollution Source Identification Method Based on Remotely Sensed Aerosol and Improved Glowworm Swarm Optimization , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Ming Li,et al.  Forecasting Fine-Grained Air Quality Based on Big Data , 2015, KDD.

[28]  Ramesh C. Jain,et al.  Integration of Diverse Data Sources for Spatial PM2.5 Data Interpolation , 2017, IEEE Transactions on Multimedia.

[29]  Junji Cao,et al.  Molecular, seasonal, and spatial distributions of organic aerosols from fourteen Chinese cities. , 2006, Environmental science & technology.

[30]  Weiwei Sun,et al.  Indoor air quality monitoring system for smart buildings , 2014, UbiComp.

[31]  Pingyi Fan,et al.  Non-Parametric Message Importance Measure: Storage Code Design and Transmission Planning for Big Data , 2017, IEEE Transactions on Communications.

[32]  Sachit Mahajan,et al.  ADF: An Anomaly Detection Framework for Large-Scale PM2.5 Sensing Systems , 2018, IEEE Internet of Things Journal.

[33]  Basic Study of Indoor Air Quality Improvement by Atmospheric Plasma , 2016 .

[34]  M. Shamim Hossain,et al.  Urban Healthcare Big Data System Based on Crowdsourced and Cloud-Based Air Quality Indicators , 2018, IEEE Communications Magazine.

[35]  Qiang Zhao,et al.  Air Quality Forecast Monitoring and Its Impact on Brain Health Based on Big Data and the Internet of Things , 2018, IEEE Access.

[36]  Wenjun Jiang,et al.  Inhalable Microorganisms in Beijing’s PM2.5 and PM10 Pollutants during a Severe Smog Event , 2014, Environmental science & technology.

[37]  Abdullah Kadri,et al.  Urban Air Pollution Monitoring System With Forecasting Models , 2016, IEEE Sensors Journal.

[38]  Shengming Wang,et al.  Investigation of the Arrayed Dielectric Barrier Discharge Reactor for PM2.5 Removal in Air , 2016, IEEE Transactions on Plasma Science.

[39]  D. Abbey,et al.  Chronic respiratory symptoms associated with estimated long-term ambient concentrations of fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) and other air pollutants. , 1995, Journal of exposure analysis and environmental epidemiology.

[40]  J. Schwartz,et al.  Effects of airborne fine particles (PM2.5) on deep vein thrombosis admissions in the northeastern United States , 2015, Journal of thrombosis and haemostasis : JTH.

[41]  Bert Brunekreef,et al.  Personal, indoor, and outdoor exposures to PM2.5 and its components for groups of cardiovascular patients in Amsterdam and Helsinki. , 2005, Research report.

[42]  Zaid Chalabi,et al.  An Exposure-Mortality Relationship for Residential Indoor PM2.5 Exposure from Outdoor Sources , 2017 .

[43]  Ajay Taneja,et al.  A Study on Indoor/Outdoor Concentration of Particulate Matter in Rural Residential Houses in India , 2009, 2009 Second International Conference on Environmental and Computer Science.

[44]  Fang Deng,et al.  The MR-CA Models for Analysis of Pollution Sources and Prediction of PM2.5 , 2019, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[45]  Qing Yu Meng,et al.  Influence of ambient (outdoor) sources on residential indoor and personal PM2.5 concentrations: Analyses of RIOPA data , 2005, Journal of Exposure Analysis and Environmental Epidemiology.

[46]  Pingyi Fan,et al.  Differential Message Importance Measure: A New Approach to the Required Sampling Number in Big Data Structure Characterization , 2018, IEEE Access.

[47]  George Percivall,et al.  DataFed: An Architecture for Federating Atmospheric Data for GEOSS , 2008, IEEE Systems Journal.

[48]  Hui Wang,et al.  A spatial-temporal model to improve PM2.5 inference , 2017, 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS).

[49]  Yung-Chung Tsao,et al.  An implementation of a particle detective mobile device to solve the problem of collecting PM2.5 data without Internet-connection , 2017, 2017 International Conference on Applied System Innovation (ICASI).

[50]  Klaus Moessner,et al.  Data-Driven Air Quality Characterization for Urban Environments: A Case Study , 2018, IEEE Access.

[51]  Jianqiang Li,et al.  Forecasting PM2.5 Concentration Using Spatio-Temporal Extreme Learning Machine , 2016, 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA).

[52]  Martin Gysel,et al.  Chemical characterisation of PM2.5, PM10 and coarse particles at urban, near-city and rural sites in Switzerland , 2005 .

[53]  Koji Zettsu,et al.  Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data , 2014, 2014 IEEE International Conference on Big Data (Big Data).

[54]  Cheng-Ta Chiang,et al.  Design of a High-Sensitivity Ambient Particulate Matter 2.5 Particle Detector for Personal Exposure Monitoring Devices , 2018, IEEE Sensors Journal.