International Journal of Environmental Research and Public Health Article Impacts of Thermal Environments on Health Risk: A Case Study of Harris County, Texas Bumseok Chun 1, Misun Hur 2 and Jaewoong Won 3,4,* 1 Urban Planning and Environmental Policy, Texas Southern University, Houston, TX 77004, USA; bum.chun@tsu.edu 2 Department of Geography, Planning, and Environment, East Carolina University, Greenville, NC 27858, USA; hurmi@ecu.edu 3 Department of Real Estate, Graduate School of Tourism, Kyung Hee University, Seoul 02447, Korea 4 Department of Smart City Planning and Real Estate, Kyung Hee University, Seoul 02447, Korea * Correspondence: jwon@khu.ac.kr Abstract: The loss of green spaces in urbanized areas has triggered a potential thermal risk in the urban environment. While the existing literature has investigated the direct relationship between ur- ban temperatures and health risks, little is known about causal relationships among key components of urban sustainability and health risks, through a pathway involving urban temperature. This study examined the multiple connections between urbanized land use, urban greenery, urban temperatures and health risks in Harris County, Texas. The census tract-level health data from the 500 Cities Project (Centers for Disease Control and Prevention) is used for analysis. Structural equation model analyses showed that the urban temperature played a mediating role in associations between urbanized land use, urban greenery and health risk. Urban vegetation is associated with a decrease in health risks,   while urban land use has associations with an increase in health risks. Findings suggest that proactive policies tailored to provide rich urban greenery in a neighborhood can alleviate urban land use effects Citation: Chun, B.; Hur, M.; Won, J. Impacts of Thermal Environments on on health risks. Health Risk: A Case Study of Harris County, Texas. Int. J. Environ. Res. Keywords: health risk; thermal environment; green infrastructure; structural equation model; land Public Health 2021, 18, 5531. use; urban environment https://doi.org/10.3390/ ijerph18115531 Academic Editor: Paul B. Tchounwou 1. Introduction The United States Environmental Protection Agency (EPA) reports that a total of Received: 23 April 2021 more than 11,000 people have died from heat-related causes in the past 40 years, which Accepted: 18 May 2021 is between 0.5 and 2 deaths per million people [1]. When considering underlying and Published: 21 May 2021 contributing causes of death, the death ratio has increased by more than threefold if we compare the 2010s to the 1980s [1]. However, unlike other disastrous natural events—e.g., Publisher’s Note: MDPI stays neutral hurricanes and tornadoes–heat-related events are not considered a disaster incident by with regard to jurisdictional claims in the US Federal Emergency Management Agency (FEMA). With frequent extreme ‘killer published maps and institutional affil- heat’ days, Sherman (2020) alerts cities to the need to develop plans for extreme heat [2]. iations. Planning Magazine also warned planners to be “heat-ready,” which is just as crucial as being storm-ready [3]. The United Nations forecasts that 70% of the world’s population will live in urban areas by 2050 [4]. Urbanized areas often experience higher temperatures than outlying Copyright: © 2021 by the authors. areas, i.e., the urban heat island (UHI) [5]. The UHI further amplifies heat-related threats Licensee MDPI, Basel, Switzerland. to city dwellers. Research has suggested the adopting vegetation and bodies of water This article is an open access article through design and land use policies as solutions to address the UHI problem in cities [6–8]. distributed under the terms and Although such findings are helpful, their scope is limited to looking at the direct relation- conditions of the Creative Commons ships between UHI and selective urban features. Thus, it is vital to investigate the complex Attribution (CC BY) license (https:// interplay among different components to understand the connections among features creativecommons.org/licenses/by/ 4.0/). more clearly. Int. J. Environ. Res. Public Health 2021, 18, 5531. https://doi.org/10.3390/ijerph18115531 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 5531 2 of 15 This paper takes a comprehensive approach by highlighting the interwoven asso- ciations among land use, urban greenery, temperature, and health risk using structural equation modeling (SEM). Below are the detailed research questions: Question 1: Which type of land use affects urban temperature, especially surface temperature? Question 2: What type of greenery features (e.g., tree canopy cover, tree height, and glass cover) influences temperature? Question 3: Does the thermal environment adversely affect public health? Question 4: Are there associations between land use, urban greenery, temperature, demographics, and health risk? If so, in which direction? Harris County in Texas, where the Houston metropolitan area sits, experiences extreme heat, which can lead to severe environmental and health-related problems. According to the Heat Surveillance Monthly Report, heat-related illness and mortality events continued to increase, from 150 cases to 256 cases during the three-year period from 2013 to 2016 [9]. Taking Harris County as the study area, we sought to elucidate the underlying meaningful associations between urban land use, greenery characteristics, urban temperature, and residents’ health—i.e., chronic disease. 2. Literature Review 2.1. Land Use Pathway for Urban Temperature and Health Studies have reported that built-up areas are critical contributors to increases in surface temperatures. Maloley (2009) analyzed land cover changes during the past two decades in Canada’s Toronto area and found a significant effect of new development on the increase in surface temperatures [10]. Other studies examined which land use types have the most influence on urban temperatures. Jusuf et al. (2007) examined the effects of land use on surface temperatures and found that commercial, residential, airport, and industrial land uses created higher temperatures than parks [11]. Rinner and Hussain (2011) analyzed various land uses (e.g., industrial, institutional, residential, open area, parks and recreational spaces, and water bodies) related to the Toronto area’s surface temperature and found that surface temperatures were higher for commercial and industrial land use, characterized by a high proportion of built-up surfaces [12]. At the same time, parks and recreational spaces had lower surface temperatures. Health studies have found significant evidence showing close associations between land use patterns and community health promotion [13,14]. These studies take a similar perspective to that of the recent Smart Growth movement in urban planning. Compact and diverse land use choices create more accessible destinations that encourage walking trips as a way to promote a healthy lifestyle [15]. A modal shift from private vehicles to walking and public transit has been encouraged by central planning policies. However, the history of modern urban development has shown high-density land use and built environments being the leading causes of rising urban temperatures. Research has revealed that urban heat can increase mortality risks such as respiratory illness, heart disease, and cardiovascular disease [16]. However, the link between land use and health risk through urban temperature pathways is not yet clear. This study examines the associations between various land uses and health risks, through urban temperature as the mediator. 2.2. Urban Greenery Pathway for Urban Temperature and Health Among various land uses, green spaces—i.e., parks and open spaces—have received attention in the literature due to the various potential benefits they can bring. Empirical research has found evidence that green spaces improve urban environmental quality— including air, noise, and temperature [17]. Green spaces also boost residents’ psychophys- iological well-being through their therapeutic and stress-relieving effects [6]. Greenery promotes healthy behaviors (i.e., walking in a neighborhood) [18], which foster substantial social capital as an opportunity for walking and social interactions [19]. Research confirms the benefits of strong social capital for psychological resiliency [20]. Gill et al. (2007) Int. J. Environ. Res. Public Health 2021, 18, 5531 3 of 15 highlighted the critical role of green infrastructure as a vital adaptation strategy for climate change in urban environments [21]. Such green benefits including social, psychological, and environmental aspects have positively influenced community health. The literature suggests that residents living in a community with an abundance of greenery tend to have better health (e.g., less morbidity, less mortality) and well-being (e.g., happiness) [22]. They benefit from clean air, more opportunities for physical activities, recovery from mental stress, and social interaction [23,24]. Residents in a greener neighborhood had higher satisfaction with their environment, which positively relates to mental health [25] and physical health [26]. Research reported that people in green environments—compared to those who are not— have lower blood-pressure-related problems [6]. They are also less overweight/obese [27], have lower morbidity and mortality ratios [19], and have fewer cardiovascular disease cases [28]. Despite the numerous benefits of urban greenery, it is unclear which green features contribute to urban temperature and health. The greenery studied is often limited to an urban park or open space [29]. Although the value of parks and open spaces is immense, other greenery features, such as street trees and grass in a neighborhood, could bring more or less direct and significant benefits to residents. Greenery such as street trees, plants, and grass alongside or on sidewalks may have significant potential to support planning strategies for establishing more relaxed environments. At the same time, they are still effective contributors to creating walkable and healthy neighborhoods. Our study takes various greenery features into consideration and searches for specific urban greenery features that alleviate urban temperatures and improve residents’ health. 2.3. Multiple Pathways among Land Uses, Green Spaces, Urban Temperature and Health Risk The outdoor temperature (high or low) affects health directly and indirectly. High urban temperatures, predominantly during heat waves, adversely influence health. Heat- waves intrude on nervous system functions and can lead to the termination of vital rhythmic activity as they obstruct the balance of nerve signals with abnormal electrolyte levels [30]. Heat exposure can also increase chronic disease risks—e.g., obesity, high blood pressure, stroke, and asthma [31]. A chemical reaction in the atmosphere assisted by hot tempera- tures worsens air quality, and this may cause outdoor discomfort relevant to asthma [32]. High temperatures can hinder outdoor physical activity [33] and might adversely impact the body’s metabolism rate. Edwards et al. (2015) also confirmed reductions in physical activities for every ten additional degrees of heat in young children in their longitudinal cohort study [34]. A lack of such physical exercise might cause obesity and high blood pressure [35]. While these reports have addressed the importance of thermal environments from the community health viewpoint, empirical evidence needs to be provided. In this study, we examine whether and how land use and green space are associated with health risk through the pathway of urban temperature. The interwoven associations among urban environmental aspects and health are thoroughly investigated. 3. Study Area This study focused on Harris County (area: approximately 4602.41 km2), Texas, where Houston sits. Figure 1 shows the geographic location of Harris County. With over two million people as of 2010, Houston is the most populated city in Texas. The U.S. Census’s estimated population of Houston (approx. 2.3 million) ranks it as the fourth-largest urban area in the United States, after New York City (approx. 8.3 million), Los Angeles (approx. 4.0 million) and Chicago (2.7 million) [36]. The Houston-The Woodlands-Sugar Land Metropolitan Statistical Area has grown significantly in recent decades from 1 million residents in 1950 to 3.3 million in 1980 [37] and 7.1 million in 2019, with increased built-up urban areas [36]. Int. J. Environ. Res. Public Health 2021, 18, x 4 of 15 Int. J. Environ. Res. Public Health 2021, 18, x 4 of 15 Land Metropolitan Statistical Area has grown significantly in recent decades from 1 mil- Int. J. Environ. Res. Public HeaLltahn2d02 1Ml,i1oe8tn,r5 o5rp3e1soilditeannt sS tiant i1s9ti5c0a lt oA r3e.3a mhaisll igorno winn 1s9ig80n i[f3ic7a]n atnlyd i n7. 1re mceinllti odne ciand 2e0s1 f9r,o mwi 1th m inilc-re4aosfe1d5 lion rebsiudieltn-utsp i nu r1b9a5n0 a troe a3s.3 [ 3m6]i.l lion in 1980 [37] and 7.1 million in 2019, with increased built-up urban areas [36]. FFiiggurree 11.. Looccaattiioon ooff Haarrrriiss Coounttyy iin Teexxaass.. Figure 1. Location of Harris County in Texas. FFiigguurree 22 shshoowws sthteh elolnognigtuitduidnianl atlretnredn odf olafnlda nudseu csoemcpoomsiptioosnitsi o(lnesft)( laenftd) atenmdpteerma-- Fitgpuuerrreae (t r2ui rgsehot()rw i(gdsh attth)ae (r dleoasntoaguirrtecuesdosi:un trahclee t sUr:e.nSthd. eG oUef o.lSlao.ngdGic euaosl leSo ugcoricvmaelpySo (usUirtSvioGenySs) ( (UalneSfdGt) t Sha)nea dHn tdoeumtshpteoenrHa-G-oualsvtoesn-- ture (ritGgohnatl v)A e(rdsetaoat naC AroeursenoaucCirlc)o.e usTn:h ctehil e)l.e UTft.h Sce.h lGaerfetto cslhohgaoriwctassl h tSohuwer svinetchyre e(aiUnsScerG einSa )sd eaenivnded ltoehpveee Hldo oplauensdtdol anun-sdGesau ldsveesfsid-neefidn beyd ton Artebhaye C tNhoaeutniNocnailat)il.o LTnahaneld Ll eCafnto dvcheCra orDtv asethrao D(wNasLt aCth(DNe )iL wnCchrDiel)aes wiet hsihnilo edwietsv sethhlooep wdeesdc trlheaeansdde e uicnsr eefosa rsdeesetif nicnofevoder erbasygt ec oovveerr- the Nataimgoenea oilnv L eaHrnatdirm rCiseo CvineoruH Dnaatrytra.i sT(NhCeLo CgurDnat)py w.h hToihnlee t ihgte rs ahrpioghwhtso nsthteoh wdeesrc itrgheheat siens hicnroe wfaossretdsht etceoimnvcperreearagaseteu odrvetese rdm upreinrag- time intt huHera esrsarmdisu eCr iponeugrnitothyde. . TsHahmoet g etrepameprphio eordna.t tuHhreo str itmgemhatkp seeh roauwttudsr oethoserm ainackctireveioatsiuetsdd moteoomrreap cedtriiavfftiiutcirueeslstm , deouxrraeicnedgri bffiactiunlgt, the samdeixes acpcoeemrribfoaodtri.tn .H godti stecommpfeorratt.ures make outdoor activities more difficult, exacerbating discomfort. FFiigguurree 22.. LLaanndd uussee aanndd aannnnuu aall tteemppeerraattuurree vvaarriiaattiioonnss iinn Haarrrriiss CCoouunnttyy.. Figure 2. Land use and annual temperature variations in Harris County. 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Methodology 4.1. Data Sources, Factors and Variables Four chronic diseases—obesity, high blood pressure, stroke, and asthma— were used as the primary endogenous variables for the Health Risk factor. We obtained these data from the 500 Cities Project sponsored by the Robert Wood Johnson Foundation and Centers for Disease Control and Prevention (CDC) in 2015 (https://www.cdc.gov/500cities/index.htm (accessed on 23 April 2021)). The 500 Cities Project initially used publicly available data on Int. J. Environ. Res. Public Health 2021, 18, 5531 5 of 15 27 types of chronic diseases at the city and census tract levels. Each variable ranged from 0% to 100% of the population for each disease per census tract. Our research employed chronic disease data from the 634 census tracts in Harris County, omitting the 152 census tracts with no data reported. For the Land Use factor, we adopted the parcel data from the Harris County Ap- praisal District (HCAD). This included ten categories of land use type: single-family residential, multi-family residential, commercial, office, public/institutional, industrial, transportation/utility, park/open spaces, undeveloped, and agricultural uses. However, we re-grouped them into five land use patterns based on similar environmental character- istics to reduce statistical bias caused by the high degree of similarity between land uses. Then, we calculated the percentage coverage by each category from all available land uses per each census tract. For the Urban Greenery factor, we applied an advanced geospatial analysis. Light Detection and Ranging (LiDAR) was used for vegetation height, and high-resolution Color-infrared (CIR) aerial photography data was used for the density of green space. Proxy tree heights generated from the LiDAR were applied to cells with Normalized Differential Vegetation Index (NDVI) value greater than 0.2, representing green space [39]. We estimated vegetation densities with the average tree height of each census tract. Heights greater than 3.05 m (?10 feet) were treated as trees (canopy), and others were considered as grasslands covering all types of vegetation, such as grassland/herbaceous, shrub/scrub, and pasture/hay [40]. We used the 2008 LiDAR and the 2014 CIR image data produced by the Houston-Galveston Area Council (H-GAC). The CIR images were provided with a 3-m horizontal resolution, which can capture tree canopies in parks and along the streets. Although there were discrepancies in data sources due to data availability issues, it did not create any data construction problems because there has been little construction and demolition in the study area. Thermal environments, directly and indirectly, affect human comfort and health in terms of heat-related mortality and morbidity. We measured two temperature variables— average Daytime Land Surface Temperature (DLST) and average Nighttime Land Surface Temperature (NLST)—using the MODIS (Moderate Resolution Imaging Spectroradiometer) MOD11A2 product at 1-km spatial resolution (https://earthdata.nasa.gov/ (accessed on 10 January 2019)). Satellite-derived temperatures can be a way to remove uncertainty re- garding missing information from weather monitoring stations. In this study, we employed the average DLST and NLST measured in 2014. Various demographic characteristics were also considered. A meta-analysis conducted by Romero-Lankao et al. (2012) identified diverse socio-demographic variables supporting the association between temperature and heat-related diseases [41]. Of these, we selected three that could avoid multicollinearity between variables. First, a different racial distri- bution could cause different exposure levels to health risk because of different physical, mental, and social activity. Second, education level is also a key factor to determine poten- tial exposure to health risk because it affects adaptive functional capability in terms of a healthy lifestyle. Last, the elderly has been classified as a vulnerable population, imply- ing that they could be affected by the thermal environment. All demographic data were retrieved from the U.S. Census (2014 American Community Survey 5-year estimates) [42]. 4.2. Statistical Analysis We used the SEM analysis with Covariance Analysis and Linear Structural Equations (CALIS), using the maximum likelihood method of parameter estimation with SAS 9.4. The SEM is a hybrid model with a two-step process: first, a measurement model and then a structural path model [43]. The measurement model was used to describe the relationships between the latent factors and their indicator variables. A confirmatory factor analysis (CFA) was used for the procedure. Three goodness-of-fit indexes determined the model fit with the variables and factors and their interwoven associations. For more information, see Table 2. We used five factors with 18 variables (Table 1) and the variance–covariance matrix Int. J. Environ. Res. Public Health 2021, 18, 5531 6 of 15 (n = 634). Among the factors, land use, urban greenery, and demographics were exogenous factors, while temperature and health risk were endogenous factors in the suggested SEM model. 5. Findings 5.1. Descriptive Statistical and Comparative Spatial Analyses The descriptive analyses using the means and the spatial distributions of each variable enabled us to examine Harris County’s circumstances in 2014 at the census tract level. A summary of all factors and variables used in this research can be found in Table 1, with simple descriptive statistics, units, and descriptions. Table 1. Factors, Variables, and Descriptive Statistics. Factor Variable Mean SD Min Max Description Obesity 30.93 11.48 0 51.1 The population with obesity (%) High Blood 29.68 11.08 0 54.7 The population with high bloodHealth Risk Pressure pressure (%) Stroke 2.82 1.59 0 8.5 The population with stroke (%) Asthma 7.87 2.71 0 14.3 The population with asthma (%) Residential 55.76 17.54 0 98.76 Single-family and multi-familyresidential land use (%) Land Use Commercial 16.54 11.80 0 100 Commercial, office, and public/institutional land use (%) Industrial 7.09 9.19 0 65.77 Industrial land use (%) Utility 2.69 6.72 0 97.53 Transportation and utility landuse (%) Other land use, i.e., parks, open Other 17.88 13.67 0 79.76 space, agricultural land,undeveloped and unclassified land use (%) Tree Cover 13.33 8.88 0 0.626 Tree canopy cover (%) Urban Greenery Grass Cover 26.59 9.67 4.32 59.75 Grassland cover (%) Tree Height 21.01 4.07 14.8 37.3 Average tree heights (m) Annual average vegetation NDVI 0.45 0.08 0.25 0.24 coverage based on land surfacereflection of satellite images (no unit: 0–1 range) Temperature DLST 30.01 1.38 23.92 32.29 Average annual daytime land surface temperature (?C) NLST 17.83 0.65 15.67 19.01 Average annual nighttime landsurface temperature (?C) Non-Whites 39.37 22.27 0 54.40 Non-whites (%) Demographics Bachelor+ 18.87 17.60 0.30 67.68 People with bachelor’s or higherdegree (%) 65+ 9.64 5.18 0 30.50 People 65+ years old (%) First, the percentage of the population with obesity or high blood pressure was significantly higher than those with asthma or stroke. Second, the majority of Harris County land use in 2014 was for residential use, followed by commercial and other land use (likely to be green-related land use). Third, the mean value of the grass cover was close to double that of tree cover. As for demographic characteristics, compared to national averages, the county had more non-white, highly educated, and older population (compared to 26.2% non-whites, 17.2% for bachelor or higher education attainment, and 13.7% people 65+ age-old, nationally). Figure 3 visualizes the correlation matrix of variables as a heatmap. The map helps to identify the incidence patterns as well as the anomalies among the variables. Yellow means positive, and blue means negative. The stronger the color, the larger the correlation magnitude. One outstanding finding was that all health risk variables turned out to be positively correlated to each other (box A in red). In contrast, there seemed to be negative correlations between temperature variables and urban greenery variables (boxes B in black). However, the strengths of the associations of health risks with land use, urban greenery, temperature, and most demographic variables (boxes C in white) were unclear. Int. J. Environ. Res. Public Health 2021, 18, x 7 of 15 positively correlated to each other (box A in red). In contrast, there seemed to be negative correlations between temperature variables and urban greenery variables (boxes B in Int. J. Environ. Res. Public Health 2021, 18, 5531 7 of 15 black). However, the strengths of the associations of health risks with land use, urban greenery, temperature, and most demographic variables (boxes C in white) were unclear. B C B C A C C Figure 3. Correlation Heatmap. Figure 3. Correlation Heatmap. To explore each factor’s local patterns, we further looked at the spatial distributions of the variables. Figure 4 illustrates the spatial patterns of each urban greenery variable at Toth excepnslousrtera ectalcevhe l.faFicrstto, trh’es NloDcVaI—l preaprtetseernntinsg, twheev efguertatthioenrc olvoeorakge—d haadt tsihmeil asrpatial distributions of the vspaartiiaalbdlisetsri.b Futiigonusrtoe g4r aislslucosvterr.aTtheesc tehntera sl peaastt–iwaels pt baatntderwnitsh loofw eeravcahlu eusrfborabno tgh reenery variable at features is the Buffalo Bayou, a slow-moving river that flows through Houston. Second, the centshue ssp tartiaacl tp altetevrensl.o Ff tirresetc,o tvheer a NndDtrVeeIh—eigrhetpwreeresesinmtiilanr,gw tihthet hveewgheotlae tsipoano cfothveerage—had similar spatial sdouisthterrinbeudtgieoonfst hteos tgurdaysasr ecaohvaveirn.g Tlohwee rcveanlutresa,lw ehailsett–hwe neosrtth beaasntedrn wareitahs hlaodwer values for both featurehsi gihse rtvhaelu Besu. Tfhfaerleow Basaaypopruox, iam ately 13.3% more grass coverage than tree coverage inthe study area. slow-moving river that flows through Houston. Second, the spatialF pigaurtete5rpnress eonfts tDreLeST caonvdeNrL aSTn. dIn t2r0e14e, theeiagvehratg we DeLrSeT swimas i3l0a.0r1, ?wC,iatnhd tthee whole span of the southeNrnL SeTdwgaes 1 ? o7.f8 3thCe instHuadrryis Caroeunat yh. aExvpienctedly, the urban core temperature—for thedowntown Houston area—remained higher thgan ltohewsuerrr ovunadluinegss,,e vwenhaitlnei gthhtteim ne.oFrotrheastern areas had higher evxamluplees, t.h Te uhrebrane cworeatse mapeprartuorxeiwmasa8t.e37ly?C 1h3ig.3h%er i nmthoerdea ygtirmaesasn cdo3v.34er?Caghieg htehran tree coverage in the stuidnyth earneigah.t ti me than its surroundings. Figure 6 maps the percentage of the population with each health risk variable per census tract. Relatively high health risk areas are commonly observed in the northeast, northwest, and southern directions from downtown Houston. The census tracts in both orange and red for obesity and high blood pressure indicate that more than 40% of the population had obesity and hypertension symptoms during the research year, which is a very high percentage and indicates significant public health problems. Int. J. Environ. Res. Public Health 2021, 18, 5531 8 of 15 Int. J. Environ. Res. Public Health 2021, 18, x 8 of 15 (a) Tree Cover (%) (b) Grass Cover (c) Tree Height (m) (d) NDVI Int. J. Environ. Res. Public Health 202F1,i g18u, x Figure 4. SpatiarleP 4a.t tSeprnatsiaolf PUartbtearnnGs oref eUnrebrayn( NGoretee:nbelruye (cNiroctlee: lbolcuaet ecsirdcolew lonctoawtesn dHoowunsttoown)n. Houston). 9 of 15 Figure 5 presents DLST and NLST. In 2014, the average DLST was 30.01 °C, and the NLST was 17.83 °C in Harris County. Expectedly, the urban core temperature—for the downtown Houston area—remained higher than the surroundings, even at nighttime. For example, the urban core temperature was 8.37 °C higher in the daytime and 3.34 °C higher in the nighttime than its surroundings. Figure 6 maps the percentage of the population with each health risk variable per census tract. Relatively high health risk areas are commonly observed in the northeast, northwest, and southern directions from downtown Houston. The census tracts in both orange and red for obesity and high blood pressure indicate that more than 40% of the population had obesity and hypertension symptoms during the research year, which is a very high percentage and indicates significant public health problems. (a) DLST (°C) (b) NLST (°C) Figure 5. SpaFtiiaglu preat5te. rSnpsa otifa tlepmaptteerrantsuoref.t e mperature. (a) Obesity (%) (b) High Blood Pressure (%) (c) Stroke (%) (d) Asthma (%) Figure 6. Spatial patterns of health risk (Note: Blue circle locates downtown Houston). 5.2. Measurement Model Table 2 lists the Goodness of Fit Indexes of three models—our initial theoretical model, the measurement model, and the SEM model. Among various fit indexes, we used the Goodness of Fit Index (GFI), the Bentler Comparative Fit Index (CFI), and the Bentler- Bonett Normed Fit Index (NFI). With the lower risk of producing biased estimates in small samples [44], researchers often use these indexes. Values over 0.9 on its index indicate an acceptable fit. Since the initial theoretical model had a poor fit, we revised the model. Int. J. Environ. Res. Public Health 2021, 18, x  9  of  15        Int. J. Environ. Res. Public Health(2a0)2 1D, 1L8S, 5T5 3(1°C)  (b) NLST (°C)  9 of 15 Figure 5. Spatial patterns of temperature.        (a) Obesity (%)  (b) High Blood Pressure (%)      (c) Stroke (%)  (d) Asthma (%)  Figure 6. SFpigautirael  p6.a Sttpearntisalo pf ahtetaelrtnhsr oisfk h(eNalothte r: iBsklu (eNcoirtcel:e Blloucea tceirscdleo lwocnatotews ndoHwonutsotownn) .Houston).  55..22.. MMeeaassuurreemmeenntt MMooddeell  TTaabbllee  22  lliissttss  tthhee GGooooddnneessss  ooff FFiitt  IInnddeexxeess ooff  tthhrreeee mmooddeellss——oouurr  iinniittiiaall  tthheeoorreettiiccaall  mmooddeell,, tthhee mmeeaasusurermemenent tmmodoedle, la,nadn tdheth SeESME Mmomdeold. Aelm. oAnmg ovnagriovuasr ifoitu isnfidtexinesd, ewxees u, swede  tuhsee GdothoednGeosso donf Fesits IonfdFeixt (IGndFIe)x, t(hGeF BI)e,ntthleerB CeonmtleprarCaotimvep Farita ItnivdeeFx i(tCIFnId)e, axn(dC FthI)e, Baenndtltehre? BBoennetltetr N-BoornmeettdN Foitr ImneddexF (itNIFnId).e Wx (iNthF tIh).eW loiwthetrh reislko wofe pr rroisdkuocfinpgr obdiausceidn gesbtiiamsaedtese sinti msmataelsl  sinamsmplaelsl [s4a4m], prelessea[r4c4h],errse soefatercnh uesres tohfetesne iunsdeetxheess. eVianludeesx eosv.eVr a0l.9u eosno ivtse irn0d.e9xo innditiscainted aenx  ainccdeipcatateblaen  faitc.c Sepintcaeb ltehfie t.inSiitniacle  tthheeoirneittiicaallt hmeoodreetli chaaldm ao dpeolohra dfita, pwoeo  rrefivti,swede  trheve ismedodthele.  model. Hatcher (1994) suggests reassigning or altogether dropping an indicator from a model rather than assigning it to two factors simultaneously (maintaining the unifactorial   characteristics of each indicator variable) [45]. After a series of modifications using the CFA analysis, we found a model in which all fit indexes fell into a reasonable error of approximation (Table 2). Therefore, the model was tentatively accepted as the study’s final measurement model, and several tests were conducted to assess its reliability and validity (Table 3). Table 2. The goodness of fit indexes of three models. Model N GFI CFI NFI Initial theoretical model 634 0.58 0.67 0.66 Measurement model 634 0.90 0.91 0.90 SEM model 634 0.90 0.91 0.90 Int. J. Environ. Res. Public Health 2021, 18, 5531 10 of 15 Table 3. Factors and measures of the measurement model. Factors and Standardized Convergent Reliability Variance ExtractedMeasurements Factor Loading Validity (t) a Estimate Health Risk 0.946 b 0.854 Obesity 0.88 84.96 0.774 High Blood Pressure 0.96 149.60 0.922 Stroke 0.93 122.10 0.865 Land use 0.056 b 0.286 Commercial 0.66 9.60 0.436 Utility ?0.37 ?7.42 0.137 Urban Greenery 0.831 b 0.714 Tree Cover 0.93 34.00 0.865 Tree Height 0.75 27.07 0.563 Temperature 0.801 b 0.679 DLST 0.98 30.74 0.960 NLST 0.63 20.03 0.397 a All t-tests were significant at p < 0.001. b Denotes composite reliability. The measurement model resulted in only nine indicator variables being significant among the suggested 18 variables. Among the land use variables, only commercial and utility were significant; for urban greenery, variables related to trees were significant; and for health risk, all variables except asthma were found to be significant. The model indicated that both the DLST and NLST variables were significant. However, none of the demographic variables were validated in the associations with other latent variables. As a result, we excluded all invalid variables and the demographic factor. Table 3 shows the measurement model’s factors and indicators with the results from the reliability and validity assessments. The Standard Factor Loadings for each variable are presented in the second column of the table. The health risk, urban greenery, and temperature factors showed higher than 70 Composite Reliability, which reflects the internal consistency of the indicators measuring the given factor [46]. A Composite Reliability value of 70 was considered as being the minimum acceptable level of reliability for the instruments. Hatcher (1994) suggested the use of Variance Extracted Estimates to “assess the amount of variance that is captured by an underlying factor in relation to the amount of variance due to measurement error (p. 331)” [45]. Similar to reliability assessments, all factors except land use reported higher than the acceptable level of 50. The findings could be interpreted such that, for example, 85% of the variance was captured by the Health Risk factor, and only 15% (1 ? 0.85 = 0.15) was due to measurement error. As explained, most reliability and validity test findings generally supported the factors and their indicators. Although both the reliability and the variance extracted estimates did not advocate land use and its indicator variables, both variables’ Convergent Validity (t-scores) were significant at p < 0.001. This confirmed that the indicator variables—commercial and utility—effectively measured the same construct—land use factor [43]. Therefore, the measurement model was taken to proceed further in the structural path model estimations. 5.3. Structural Path Model (SEM Model) Figure 7 shows the final SEM model with the estimated coefficients among all factors and variables suggested by the measurement model. In the SEM model, we visualize factors using ovals, and the variables have rectangular shapes. The exogenous factors are on the left, while the endogenous factors are on the right to show the associations/effects naturally flowing from left to right on the model. The R-squared values are also provided outside of every endogenous variable to explain how much each endogenous variable contributes to supporting a latent variable. The single-headed straight arrows represent the associations’ directional strength, while the double-headed curved arrows show the reciprocal correlations. Int. J. Environ. Res. Public Health 2021, 18, x 12 of 15 for commercial land use were covered by impervious pavements near parking lots, road- Int. J. Environ. Res. Public Health 2021,w18a,y55s3, 1and buildings, thereby reducing green spaces. In general, the implementat1i1onof 1o5f utility land use for transportation and conventional utility also reduced vegetation. Figure 7. The Final SEM Model. (Note: *: significant at 95% confidence level, **: significant at 99% confidence level). Figure 7. The Final SEM Model. (Note: *: significant at 95% confidence level, **: significant at 99% confidence level). The structural model shows the direct and indirect associations among factors. A direct a6.s sDoicsicautisosnioanp p ears as an arrow between temperature and health risk factors, between landUuseinagn dputebmlicplye ravtuarileafbalcet osersc,oannddarbye tdwaetaebnauserbs,a wn eg rceoenderuyctaendd at esmerpieesr aotfu raedfvaacntocersd. RGeISad aenraslcyasnesa blsyo afpolplolywintgh ereimndoitre csteansssioncgi ateticohnnsibqeutews.e Uensifnagct dorescthrirpotuivgeh satamtiesdtiicaatli nagndfa scptoar-. Ftioarl edxiastmripbluet,ihoena latnharliysskeasn, dwlea nedxpulsaeinfeadct othrse atrreenondlsy oafs saollc ivaaterdiaibnldesir etoct lpyrovviaidtem ap geeranteurrael ausntdheersmtaenddiaintogr .oSf itmheil asrtluyd, ayn airneda.i rAecllt avsasroiacbialetiso nwbereetw theen hspeatltiahllryis jkoiannedd utrobgaenthgerre eant ethrye ceansbues ftoraucntd levieal tteom rupner antu ardevaasntcheedm steadtiisattiocra.l Tahnealdyisriesc. tTahses oScEiMati orensuolfths eraevlteharleisdk iwntietrh- twemovpeenr arteularteiohnasdhiapsp aomsitoivneg elaffnedc tuosfe,m uordbaenra gteresetnr enryg,t hte(m0.p1e4r)a. tuTrhee, afind ihnegasltshh roiwsk tfhaact- ptoerosp. le who lived in an area with relatively higher nighttime and daytime temperatures—a thermTahlise nrvesireoanrmche’sn tm—oaslts osisghnoifwiceadnth figinhdeirnign ciisd tehnec evsitoafl oroblees iotyf ,thhieg htemblopoedraptureress fuarcet,oarn ads sthtreo kmee. dOiauttodro boertawcetievni tybortehla ltainngd tuosteh earnmd aul rdbiascno gmrfeoerntecroyu aldnds ighneaifiltcha nritslyksc.o Tnhtrei bruesteulttos tshheowin ctrheaats ainregaps rwevitahle hnicgehoefr tchoemsemheeraclitahl rainskds i.nAdlul setsrtiiaml alatnedd puasrea rmateitoesr sw, iinthc lluedssin tgrede icreacnt-, ionpdyi raencdt, samndaltloerta tlreeeffse wctes,rea rmeosruem limkealryi ztoed hainveT arebllaeti4v.ely higher land surface temperatures throughout the day. The associations further contribute to residents developing higher Tinacbildee4n. cEessti omfa otebdespiatyra, mhiegtehr sblinoothde pfirneaslsSuEreM, amnodd setlr. oke. Additional highlights can be stated. Among various land use types, only commerDciiarel catnd utility Iunsdeisr etcutrned out toT obtea lsignifi- cant tFyropmes Ftahcatot rcontributeTdo tFoa citnocrreasing tAhses olacniadti osnurface Atesmsopcieartiaotnure regAasrsdolceisasti onf the time oLf adnadyu. sSeuch land uTesme ptyepraetsu rientensify the0 .3U9HI effect through vast open 0g.r3o9unds— e.g.U, rpbaarnkGinrgee nloetrsy—oftenT ecmovpeerraetdu rbey imperv?io0u.4s3 concrete surfaces. Air cond?it0i.o43ning in commLeracnidalU bsueildings alsHo edailftfheRrsis dkramatically from cooling in0 h.0o5mes. Commer0c.0ia5l build- ingUs rabraen gGerneenraelrlyy largerH, euasleth mRiusckh energy, and ventilate a ?si0g.0n6ificant volu?m0e. 0o6f heat. HencTee,m thpeeirra tuurrbean tempHereaatluthreR iismkpacts could0 .1b4e detrimental, considering the0 .u14niversal prevalence of such land uses in the urban core. AThsism reensteiaorncehd fuabrtohveer, cboontthrilbauntdesu tsoe tahned liuterrbaatnurger ebeyn eloroykhinagd aint ddiirfefcetreansts ogcrieaetnioenrys wfeiatthurheesa sltehprairsaktetlhyr—oui.geh., tthreeem caendoiaptyin cgofvaecrt,o trreoef theemigphetr, agtruarses. cTohveerfi, nadnidn gthses uNgDgeVsIt. tAhlatt- lhaonudguh sgeriansds eceodvehraasgae pseoesmitisv etoa sbseo cai adtoiomninwaintht fheeaatlutrhe rtihskast .dIentepramrtiinceusl atrh,et hNeDarVeIa mwoitrhe higher proportions of commercial and industrial land use reported higher daytime and nighttime temperatures with a large effect size of 0.39 and higher incidences of health risks with a small effect size of 0.05 (indirect effect calculated by multiplying 0.39 and 0.14). On the other hand, urban greenery has negative associations with temperature and health risks. Areas with higher tree canopy coverage and taller (mature) trees show significantly Int. J. Environ. Res. Public Health 2021, 18, 5531 12 of 15 lower temperatures throughout the day. The effect size between urban greenery and temperature was the greatest (?0.43) among all associations in the model. The findings further contribute to the relationship between urban greenery and health risk—the indirect association between these factors was ?0.06 (calculated by ?0.43 × 0.14). It is important to note that these relations could never be disclosed using traditional linear regression analysis because none of the mediating variables would be included. The advanced SEM analysis handled the tasks perfectly, which is an additional contribution of this research to current understanding in the field. Lastly, the SEM model also showed a negative correlation (?0.23) between two exoge- nous factors—land use and urban greenery. This link indicates that areas with commercial and utility land uses often had a lower degree of tree canopy coverage and relatively smaller trees in terms of height. For example, the vast majority of ground surfaces used for commercial land use were covered by impervious pavements near parking lots, roadways, and buildings, thereby reducing green spaces. In general, the implementation of utility land use for transportation and conventional utility also reduced vegetation. 6. Discussion Using publicly available secondary databases, we conducted a series of advanced GIS analyses by applying remote sensing techniques. Using descriptive statistical and spatial distribution analyses, we explained the trends of all variables to provide a gen- eral understanding of the study area. All variables were then spatially joined together at the census tract level to run an advanced statistical analysis. The SEM results re- vealed interwoven relationships among land use, urban greenery, temperature, and health risk factors. This research’s most significant finding is the vital role of the temperature factor as the mediator between both land use and urban greenery and health risks. The results show that areas with higher commercial and industrial land use ratios with less tree canopy and smaller trees were more likely to have relatively higher land surface temperatures throughout the day. The associations further contribute to residents developing higher incidences of obesity, high blood pressure, and stroke. Additional highlights can be stated. Among various land use types, only commercial and utility uses turned out to be significant types that contributed to increasing the land surface temperature regardless of the time of day. Such land use types intensify the UHI effect through vast open grounds—e.g., parking lots—often covered by impervious concrete surfaces. Air conditioning in commercial buildings also differs dramatically from cooling in homes. Commercial buildings are generally larger, use much energy, and ventilate a significant volume of heat. Hence, their urban temperature impacts could be detrimental, considering the universal prevalence of such land uses in the urban core. This research further contributes to the literature by looking at different greenery fea- tures separately—i.e., tree canopy cover, tree height, grass cover, and the NDVI. Although grass coverage seems to be a dominant feature that determines the NDVI more than trees, only tree cover (canopy) and height (size) were significant contributors to Harris County’s land surface temperatures. Considering the positive effects of greenery on environmental and health benefits, we suggest that proactive urban policies and additional actions to plant more tall and leafy trees are pursued. Urban neighborhoods filled with abundant green features with various tree types, maturities, and coverages would further promote environmental sustainability as a related benefit, which could lead to a healthy community. Another strength of this research is the broad applicability of its methods. Since we used publicly available data searching for factors that contribute to public health, researchers can apply the techniques in other areas. Advances in SEM analysis would add strength to the research. Indirect relationships among factors cannot be projected using traditional linear regression analysis. The other strength is the geographical unit of analysis. Previous health literature often utilized aggregated information on a geographically large scale due to the issue of privacy [47,48]. Therefore, their findings are difficult to suggest local health Int. J. Environ. Res. Public Health 2021, 18, 5531 13 of 15 implications such as spatial patterns of health outcomes. In our research, we presented the potentials of using census tract-level detailed data from the CDC. Since the findings are specific to the environmental characteristics of Harris County, the implications are also explicitly related to local health needs. The research limitations could highlight directions for future research. First, inter- estingly, none of the demographic characteristics was associated with any other factor in this study. Perhaps the choice of demographic measures did not fit, or unknown variables prevented us from understanding the associations between the demographic factor and all other factors. Second, we did not take the temperature threshold or heat durations into consideration. Since the study area is one of the hottest cities throughout the year in the USA, we suspect that the temperature threshold and heat durations could influence the residents’ health conditions in various ways. Third, our study is empirical research with a specific interest in Harris County, TX. The model could be applicable in other cities with similar characteristics–megacities. Future research with comparisons could be advanta- geous. Lastly, we only used the LST as a representative variable of urban temperature associated with health. However, we understand that many other meteorological variables can be just as crucial to human health. With the increase in urban temperatures, changes in other variables such as humidity, barometric pressure, precipitation, and UV radiation can occur. Schneider and Breitner (2016) stated that the interplay of temperature with air pollution is also critical. Future research to address these limitations is needed [49]. 7. Conclusions This study used satellite image and census tract-level health data to examine com- plex associations among land use, urban greenery, surface temperatures, and health risks. This study found that the urban land uses covered mainly by concrete surfaces of building contributed to health risks whereas urban greenery such as trees along the streets and between buildings were significant for decrease in health risks. The findings of this research align with recent innovative initiatives, including the Sustainable Sites Initiative (SSI, http://www.sustainablesites.org/ (accessed on 23 April 2021)), Leader- ship in Energy and Environmental Design for Neighborhood Development (LEED-ND, http://leed.usgbc.org/nd.html (accessed on 23 April 2021)), Enterprise Green Commu- nities Criteria (https://www.enterprisecommunity.org/solutions-and-innovation/green- communities/criteria (accessed on 23 April 2021)), and the Healthy Development Measure- ment Tool (HDMT, www.thehdmt.org (accessed on 23 April 2021)). With the tremendous efforts that have been made, understanding of the adverse impacts of UHIs have grown substantially, and it seems this trend will continue in the future. For a more sustain- able, well-connected, and healthy neighborhood, continuing efforts to address this issue’s various aspects are needed. Author Contributions: Conceptualization, B.C., J.W., and M.H; methodology, B.C. and M.H.; soft- ware, B.C. and M.H.; validation, B.C., J.W., and M.H.; formal analysis, B.C., J.W., and M.H.; investi- gation, B.C.; resources, B.C., J.W., and M.H.; data curation, B.C. and M.H.; writing—original draft preparation, B.C., J.W., and M.H.; writing—review and editing, B.C., J.W., and M.H.; visualization, B.C. and M.H..; supervision, B.C., J.W., and M.H.; project administration, J.W.; funding acquisition, J.W. and M.H. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (2018R1C1B5086305). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: We deeply appreciate Burrell Montz for proofreading this paper. 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