Field or laboratory measurements are commonly conducted to determine phthalates concentrations in spaces. This study investigated the association between various influencing factors and indoor phthalates concentrations. Back-propagation (BP) Neural Network was employed to verify a prediction model of indoor phthalates concentration with 80% of experimental data and 20% remaining data. The validation of remaining data shows a reasonable accuracy for model application, where the ratios of standard deviations were all greater than 0.45, most ERMS were close to 0 and all the EMR were less than 15.5%. In addition, we used relevant data from the China, Children, Homes, Health (CCHH) study conducted in Tianjin for further inspection. The prediction on di (2-ethylhexyl) phthalate (DEHP) concentration was performed, which indicated a high accuracy. Furthermore, the Monte-Carlo simulation was applied to quantify the effect of temperature on phthalates concentration combining with the prediction model. When the temperature increment value was 4°C, the average relative decrease ratio of dibutyl phthalate (DBP), DEHP, diisobutyl phthalate (DIBP) concentration was about 7.8%, 12.8% and 9.3%, respectively. The findings have established the validity of the prediction model and provided a quantification of the influence of temperature on the concentration of phthalates in the indoor dust phase.
ShrubsoleC.On the inside the UK Government’s Clean Air Strategy in respect of volatile organic compounds in buildings. Indoor Built Environ2019; 28: 581–583.
2.
BamaiYAArakiAKawaiTTsuboiTSaitoIYoshiokaEKanazawaATajimaSJShiCTamakoshiAKishiR.Associations of phthalate concentrations in floor dust and multi-surface dust with the interior materials in Japanese dwellings. Sci Total Environ2014; 468-469: 147–157.
3.
ZhangQLuXMZhangXLSunYGZhuDMWangBLZhaoRZZhangZD.Levels of phthalate esters in settled house dust from urban dwellings with young children in Nanjing, China. Atmos Environ2013; 69: 258–264.
4.
GuoZS.Improve our understanding of semivolatile organic compounds in buildings. Indoor Built Environ. 2014; 23: 769–773.
5.
CakmakSKauriLMAndradeJDalesR. Factors influencing volatile organic compounds in Canadian homes. Indoor Built Environ May 2020. DOI: 10.1177/1420326X20926229.
6.
ZhuQQJiaJBZhangKGZhangHLiaoCYJiangJB.Phthalate esters in indoor dust from several regions, China and their implications for human exposure. Sci Total Environ2019; 652: 1187–1194.
7.
WangLXZhaoBLiuCLinHYangXZhangYP.Indoor SVOC pollution in China: a review. Chin Sci Bull2010; 55: 1469–1478.
8.
WangLXGongMYXuYZhangYP.Phthalates in dust collected from various indoor environments in Beijing, China and resulting non-dietary human exposure. Build Environ2017; 124: 315–322.
9.
SakhiAKCequierEBecherRBøllingAKBorgenARSchlabachMSchmidbauerNBecherGSchwarzePThomsenC.Concentrations of selected chemicals in indoor air from Norwegian homes and schools. Sci Total Environ2019; 674: 1–8.
10.
BuZMMmerekiDWangJHDongC.Exposure to commonly used phthalates and the associated health risks in indoor environment of urban China. Sci Total Environ2019; 658: 843–853.
11.
KashyapDAgarwalT.Concentration and factors affecting the distribution of phthalates in the air and dust: a global scenario. Sci Total Environ2018; 635: 817–827.
12.
LiangYRXuY.The influence of surface sorption and air flow rate on phthalate emissions from vinyl flooring: measurement and modeling. Atmos Environ2015; 103: 147–155.
13.
LiangYRXuY.Emission of phthalates and phthalate alternatives from vinyl flooring and crib mattress covers: the influence of temperature. Environ Sci Technol2014; 48: 14228–14237.
14.
SandbergMKabanshiAWigoH.Is building ventilation a process of diluting contaminants or delivering clean air?Indoor Built Environ2020; 29: 768–774.
15.
BiCYMaestreJPLiHZhangGGivehchiRMahdaviAKinneyASiegelJHornerSDXuY.Phthalates and organophosphates in settled dust and HVAC fifilter dust of U.S. low-income homes: association with season, building characteristics, and childhood asthma. Environ Int2018; 121: 916–930.
16.
WeiWJMandinCBlanchardOMercierFPelletierMLe BotBGlorennecCRamalhoO.Temperature dependence of the particle/gas partition coefficient: an application to predict indoor gas-phase concentrations of semi-volatile organic compounds. Sci Total Environ2016; 563-564: 506–512.
17.
SunCJZhaiZQ.The efficacy of social distance and ventilation effectiveness in preventing COVID-19 transmission. Sustain Cities Soc2020; 62: 102390.
18.
YeWZhangXGaoJCaoGYZhouXSuX.Indoor air pollutants, ventilation rate determinants and potential control strategies in Chinese dwellings: a literature review. Sci Total Environ2017; 586: 696–729.
19.
PeiJJSunYHYinYH.The effect of air change rate and temperature on phthalate concentration in house dust. Sci Total Environ2018; 639: 760–768.
20.
MusukuATanAMAwaiyeKTrabelsiF.Comparison of two-concentration with multi-concentration linear regressions: retrospective data analysis of multiple regulated LC–MS bioanalytical projects. J Chromatogr B2013; 934: 117–123.
21.
WangXHWangBZ.Research on prediction of environmental aerosol and PM2.5 based on artificial neural network. Neural Comput Appl2019; 31: 8217–8227.
22.
SunCJZhangJLHuangCLiuWZhangYPLiBZZhaoZHDengQHZhangXQianHZouZJYangXSunYXSundellJ.High prevalence of eczema among preschool children related to home renovation in China: a multicity based cross-sectional study. Indoor Air2019; 29: 748–760.
23.
LiaoCXLiuWZhangJLShiWMWangXYCaiJZouZJLuRCSunCJWangHHuangCZhaoZH.Associations of urinary phthalate metabolites with residential characteristics, lifestyles, and dietary habits among young children in Shanghai, China. Sci Total Environ2018; 616-617: 1288–1297.
24.
HuangCZhangJLSunCJLiuWZhangYPLiBZZhaoZHDengQHZhangXQianHZouZJYangXSunYXXiaZZWeschlerLBSundellJ.Associations between household renovation and rhinitis among preschool children in China: a cross-sectional study. Indoor Air2020; 30: 827–840.
25.
ChaiDBGuoXDYangDT.Method of identification abnormal data in aircraft full scale fatigue test. Eng Test2019; 59: 7–8.
26.
SunYXZhangQNHouJWangPSundellJ.Exposure of phthalates in residential buildings and its health effects. Procedia Eng2017; 205: 1901–1904.
27.
HouJZhangYFSunYXWangPZhangQNKongXRSundellJ.Air change rates in residential buildings in Tianjin, China. Procedia Eng2017; 205: 2254–2258.
28.
SunYXHouJChengRSShengYZhangXYSundellJ.Indoor air quality, ventilation and their associations with sick building syndrome in Chinese homes. Energy Build2019; 197: 112–119.
29.
CaiJLiBZYuWWangHDuCQZhangYPHuangCZhaoZHDengQHYangXZhangXQianHSunYXLiuWWangJYangQZengFBNorbäckDSundellJ.Household dampness-related exposures in relation to childhood asthma and rhinitis in China: a multicentre observational study. Environ Int2019; 126: 735–746.
30.
LiangWWangGWNingXJZhangJLLiYJJiangCHZhangN.Application of BP neural network to the prediction of coal ash melting characteristic temperature. Fuel2020; 260: 116324.
31.
ParkJJeongBChaeYTJeongJW.Machine learning algorithms for predicting occupants' behaviour in the manual control of windows for cross-ventilation in homes. Indoor Built Environ. Epub ahead of print 3 June 2020. DOI: 10.1177/1420326X20926229.
32.
YaoQHQiuBH.Prediction algorithm of gas concentration in coal mine based on improved BP neural network. Coal Technol2017; 36: 182–184.
33.
SunBLSunHZhangCNShiJWZhongDQ.Forecast of air pollutant concentrations by BP neural network. Acta Scientiae Circumstantiae2017; 37: 1864–1871.
34.
HuangCQingZYLiuWWangXYCaiJZouZJSunCJ.Analysis of bedroom infiltration ratio based on residential characteristics during winter nights in Shanghai. Energy Res Inform2018; 34: 35–42.
35.
SunCJLianZWLanL.Work performance in relation to lighting environment in office buildings. Indoor Built Environ2019; 28: 1064–1082.
36.
HuKChenQ.Ventilation optimization for reduction of indoor semi-volatile organic compound concentration based on the variational principle. Build Environ2015; 94: 676–682.
37.
ZhangQHSunYXZhangQNHouJWangPKongXRSundellJ.Phthalate exposure in Chinese homes and its association with household consumer products. Sci Total Environ2020; 719: 136965.
38.
KolarikBBornehagCGNaydenovKSundellJStavovaPNielsenOF.The concentrations of phthalates in settled dust in Bulgarian homes in relation to building characteristic and cleaning habits in the family. Atmos Environ2008; 42: 8553–8559.
39.
WeiWJRamalhoOMandinC.A long-term dynamic model for predicting the concentration of semivolatile organic compounds in indoor environments: application to phthalates. Build Environ2019; 148: 11–19.
40.
HeRWLiYZXiangPLiCZhouCYZhangSJCuiXYMaLQ.Organophosphorus flame retardants and phthalate esters in indoor dust from different microenvironments: bioaccessibility and risk assessment. Chemosphere2016; 150: 528–535.
41.
FengZYuCWCaoS.Fast prediction for indoor environment: models assessment. Indoor Built Environ2019; 28: 1–5.
42.
GhazaliNARamliNAYahayaASYusofNSansuddinNAl MadhounWA.Trans-formation of nitrogen dioxide into ozone and prediction of ozone concentrations using multiple linear regression techniques. Environ Monit Assess2010; 165: 475–489.
43.
WenLYuanXY.Forecasting CO2 emissions in Chinas commercial department, through BP neural network based on random forest and PSO. Sci Total Environ2020; 718: 137194
44.
WeiWJMandinCRamalhoO.Influence of indoor environmental factors on mass transfer parameters and concentrations of semi-volatile organic compounds. Chemosphere2018; 195: 223–235.
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