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1 Environment Interntionl 40 (2012) Contents lists vilble t SciVerse ScienceDirect Environment Interntionl journl homepge: Inequlities in cumultive environmentl burdens mong three urbnized counties in Cliforni Json G. Su,, Michel Jerrett, Rchel Morello-Frosch b,c, Bill M. Jesdle b, Amy D. Kyle 50 University Hll, Environmentl Helth Sciences, School of Public Helth, University of Cliforni, Berkeley, CA , United Sttes b Deprtment of Environmentl Science, Policy nd Mngement, University of Cliforni, Berkeley, CA 94720, United Sttes c Community Helth nd Humn Development, School of Public Helth, University of Cliforni, Berkeley, CA 94720, United Sttes rticle info bstrct Article history: Received 4 June 2011 Accepted 10 November 2011 Avilble online 3 Jnury 2012 Keywords: Air pollution Het Cumultive impcts Inequlity index Environmentl justice Low-income communities nd communities of color often suffer from multiple environmentl hzrds tht pose risks to their helth. Here we extended cumultive environmentl hzrd inequlity index (CEHII) developed to ssess inequlities in ir pollution hzrds to compre the inequlity mong three urbn counties in Cliforni: Almed, Sn Diego, nd Los Angeles. We included metric for het to the nlysis becuse exposure to excessively hot wether is incresingly recognized s thret to humn helth nd well-being. We determined if inequlities from het differed between the three regions nd if this dded fctor modified the metric for inequlity from cumultive exposure to ir pollution. This nlysis indicted tht of the three ir pollutnts considered, diesel prticulte mtter hd the gretest inequlity, followed by nitrogen dioxide (NO 2 ) nd fine prticulte mtter (PM 2.5 ). As mesured by our index, the inequlities from cumultive exposure to ir pollution were greter thn those of single pollutnts. Inequlities were significntly different mong single ir pollutnt hzrds within ech region nd between regions; however, inequlities from the cumultive burdens did not differ significntly between ny two regions. Modeled bsolute nd reltive het inequlities were smll except for reltive het in Sn Diego which hd the second highest inequlity. Our nlysis, techniques, nd results provide useful insights for policy mkers to ssess inequlities between regions nd ddress fctors tht contribute to overll environmentl inequlity within ech region Elsevier Ltd. All rights reserved. 1. Introduction Reserchers nd policy-mkers hve identified higher frequency nd mgnitude of exposures to environmentl ors in communities of color nd low-income communities (Institute of Medicine 1999; Morello-Frosch nd Shenss 2006). Such inequlities in environmentl hzrd exposures re incresingly recognized s potentil determinnts of helth disprities (Finkelstein et l., 2005; Morello- Frosch nd Jesdle, 2006; Morello-Frosch et l., 2011). Multiple environmentl hzrds my ct cumultively or interct in complex wys to mgnify their risks to humn helth (Ntionl Reserch Council, 2009). For exmple, the synergy between ozone nd other pollutnts in cusing helth effects hs been recently suggested (Muderly nd Smet, 2009). In previous work, we developed cumultive environmentl hzrd inequlity index (CEHII) to ssess inequlities by rcil-ethnic composition nd by poverty sttus in exposure to multiple ir pollutnts in Los Angeles County (Su et l., 2009c). In this pper, we extend tht method to compre inequlities in exposure Corresponding uthor. Tel.: ; fx: E-mil ddress: jsonsu@berkeley.edu (J.G. Su). to single nd multiple environmentl hzrds in Los Angeles County with those in Almed County nd Sn Diego County. The environmentl hzrds re trffic-relted ir pollution (nitrogen dioxide or NO 2 ), fine prticulte mtter PM 2.5 (erodynmic dimeter less thn 2.5 μm), nd diesel prticulte mtter (diesel PM). We brodened the method beyond ir pollution by dding metrics for het (both bsolute nd reltive mesures). Exposure to excessively hot wether is incresingly recognized s thret to humn helth nd well-being tht will likely worsen with climte chnge (Hrln et l., 2006; Ptz et l., 2005). Het-relted deths re chronic problem in rid climtes (Center for Disese Control, 2005). Summer het wves, spordic periods of elevted tempertures outside the norml rnge of climte vribility, occur throughout the world (Meehl nd Tebldi, 2004). They contribute to the globl burden of disese nd premture deths (Conflonieri et l., 2007; Huynen et l., 2001; Medin-Rmon et l., 2006). More deths re ttributed to het in temperte climtes thn in wrm climtes, probbly becuse people in temperture zones re less cclimted to high tempertures (Rey et l., 2007; Snen et l., 2007). Some reserch hs found significnt interctions between het nd high concentrtions of ir pollutnts such s ozone nd NO 2 (Bsu, 2009; Theohrtos et l., 2010; Vneckov et l., 2008). The highest /$ see front mtter 2011 Elsevier Ltd. All rights reserved. doi: /j.envint

2 80 J.G. Su et l. / Environment Interntionl 40 (2012) morbidity nd mortlity ssocited with extreme het pper to occur in cities, flling disproportiontely upon mrginlized groups, prticulrly the poor, minority popultions, nd the elderly (Center for Disese Control, 2009). Therefore, these disdvntged communities my experience disproportionte burdens from both mbient ir pollutnt exposures nd het. In this pper, we pplied the CEHII method to quntittively ssess inequlities in exposure to ir pollution nd het in three urbn counties. 2. Mterils nd methods This section first describes the dt sources used to develop metrics for ir pollution nd het, nd rce-ethnicity or socioeconomic sttus. We then explin the techniques used to clculte inequlities in exposure to single nd cumultive hzrds Air pollution We estimted NO 2 using lnd use regression models (Su et l., 2009) to model sptil vrition in trffic pollutnts for the three regions, using detiled pollution dt vilble from erlier studies (Ostro nd Kim, 2008; Ross et l., 2006; Su et l., 2009b). Becuse PM 2.5, levels vry over lrge res, nd there were limited monitoring sites vilble, we used geosttisticl interpoltion to estimte exposure to PM 2.5 bsed on network of 23 continully operting monitors (Krewski et l., 2009). Diesel PM concentrtions t the census trct level were estimted by the US Environmentl Protection Agency for 1999 (See Ntionl-Scle Air Toxics Assessment: ep.gov/ttn/tw/nt1999). Census trct level NO 2 nd PM 2.5 men concentrtions were extrcted from the corresponding model surfces. To exclude extreme outliers tht existed in the dt, ny pollutnt within census trct with z-score greter thn 5 ws removed from nlysis Absolute nd reltive summer het Incresed temperture nd rdition directly rise body temperture, nd incresed humidity slows cooling of the body by decresing swet evportion (English et l., 2009). An increse in wind speed, by contrst, increses sensible nd ltent het loss (Dikmen nd Hnsen, 2009). Therefore, high temperture, high humidity, nd low wind speed increse n individul's risk of het illness (Mloney, 1998). For summer het, we used Stedmn's (1984) pprent temperture, clculted by: T p ¼ 1:8 þ 1:07 T mb þ 2:4 P 0:92 v þ 0:042 Q where T p is the estimted pprent temperture nd T mb the mesured mbient temperture, both in C; P, v nd Q re vpor pressure (kpa), wind speed (m/s), nd solr rdition (W/m 2 ), respectively. In estimting dily het, the dily mximum mbient temperture ws used for T mb, nd dily verge vpor pressure nd wind speed for P nd v, respectively. Meteorologicl dt were cquired from the Cliforni Irrigtion Mngement Informtion System (CIMIS). Dily dt in summer months (July, August, nd September) from 123 monitoring sttions for were used to estimte summer het. Literture suggests tht when the temperture is bove 40 C, people working outside should tke extreme cution (Hrln et l., 2006). The pprent temperture exceeding 40 C ws treted s bsolute exceednce temperture (i.e., difference between pprent temperture nd 40 C). The totl bsolute extreme temperture exceednces were summrized for ech monitoring sttion for summer seson nd then divided by the number of dys with temperture mesured bove 40 C in the sme period to derive per dy bsolute temperture exceednces for tht summer seson. This vlue ws estimted for ech of the five yers nd then further verged to reflect the five-yer men per dy bsolute temperture exceednces ( C per dy). Distnce to cost (km), ltitude (degrees), nd elevtion (m) dt (Brody et l., 2008) were then used to model per dy bsolute temperture exceednces for the stte of Cliforni using dt from the 123 monitoring sttions. The modeling results were then used to predict bsolute dily temperture exceednces for ech census trct for the counties of Almed, Los Angeles, nd Sn Diego. An individul's response to het is lso conditioned by their locl climte. We thus clculted the totl temperture exceednces for ech monitoring sttion bsed on its historicl norml mximum temperture for summer seson (i.e., July, August, nd September). The totl temperture exceednces were then divided by the number of dys with temperture bove historicl norml mximum temperture in the sme period to derive per dy reltive temperture exceednces ( C per dy) for tht summer seson. The estimtions were conducted for the summer sesons nd dily reltive temperture exceednces of five-yer men were clculted nd used for our nlysis. Becuse of the lck of 30-yer CIMIS meteorologicl dt to derive historicl norml mximum tempertures for ech monitoring sttion, the historicl norml mximum temperture dt for the CIMIS monitoring sttions were derived from the U.S. Ntionl Climte Dt Center (NCDC) for bsed on the closest distnce principle. The reltive dily temperture exeednces E j for summer seson for t loction j were clculted s follows: E j ¼ 1 n X n i¼1 Tp ij T mx j where T mx j is the men historicl norml mximum temperture from the months of July, August, nd September t the j th loction. Tp ij is the i th dy pprent temperture in the three-month period exceeding the men historicl norml mximum temperture t the j th loction, nd n is the totl number of dys with pprent temperture greter thn the men historicl norml mximum temperture. An inverse distnce weighting function ws used to ssign the reltive dily temperture exceednces from the 123 monitoring sttions to the census trcts in the counties of Almed, Los Angeles nd Sn Diego Neighborhood rcil/ethnic composition nd poverty rte We selected two widely used neighborhood composition metrics. The first metric, bsed on the 2000 US Census, ws the census trct rcil-ethnic composition, defined s the percentge of non-whites in the popultion. The second metric ws the proportion of the popultion with n income less thn 200% of the federl poverty level, becuse on verge, fmilies need n income equl to bout two times the federl poverty level to meet their most bsic needs (Berstein et l., 2000). To reduce the complexity of the pper, only inequlities by rcil-ethnic composition re described in the min text. Inequlities cross neighborhood poverty grdients were similr nd re included in Supplementry Figs. A1, A2 nd A Cumultive environmentl hzrd inequlity index To mesure inequlity relted to rcil-ethnic or socioeconomic mesures, we modified concentrtion index developed for the World Bnk to estimte helth inequlities cross regions nd groups

3 J.G. Su et l. / Environment Interntionl 40 (2012) Fig. 1. Positive nd negtive inequlity curves. A positive curve indictes tht census trcts with higher percentge of White residents or lower poverty rtes hve lower shres of environmentl hzrd. The negtive curve indictes tht census trcts with lower percentge of White residents, or higher poverty rtes, ber higher proportion of the environmentl hzrds. The equlity line indictes tht environmentl hzrds re distributed eqully mong vrious groups of rce-ethnicity or mong vrious levels of poverty sttus. in popultion (O'Donnell et l., 2008) (Fig. 1). A summry mesure of inequlity is defined s twice the re between n inequlity curve nd the equlity line: n I ¼ es ðþds This mesure gives quntittive summry of inequlity mong groups, in which 0 indictes tht ll groups, or in our cse ll census trcts, hve n equl shre of environmentl burden (i.e., no inequlity), nd 1 is the highest level of inequlity, where one group or one census trct bers whole detrimentl burden. Such n index only cptures inequlities ssocited with single fctors. To mesure the socioeconomic or rcil-ethnic inequlities from multiple burdens, we clculted the cumultive environmentl hzrd inequlity index (CEHII) (Su et l., 2009c). The index uses the cumultive proportion of the popultion rnked by re-bsed rcil-ethnic composition or socioeconomic strt, strting from the most disdvntged ginst cumultive environmentl hzrd burdens. We ssumed the existence of fully multiplictive burdens (i.e., every pollutnt ws multiplictively synergistic with every other pollutnts). This methodologicl pproch integrtes multiple burdens nd socil dt into single index. Two inequlity indices in exposure to multiple environmentl burdens were investigted: one with the cumultive environmentl burdens from three ir pollutnts NO 2, PM 2.5, nd diesel PM; nd nother with the cumultive environmentl burdens from ir pollutnts nd het metrics. 3. Results This section first describes summer het predictions (i.e., the bsolute nd reltive het es bsed on dily temperture exceednces) t the census trct level, followed by trct level descriptive sttistics of rce-ethnicity nd ir pollution. The inequlities from exposure to ir pollution nd het in ech of the three regions re summrized. Within-county inequlities re compred between NO 2,PM 2.5, diesel PM, nd their cumultive environmentl burdens, followed by dding in het. Finlly, the inequlities in exposure to single nd cumultive burdens between the three counties re compred for the three ir pollutnt hzrds nd het. The t sttisticl tests re used to ssess the significnce of differences in ll the inequlity indices discussed Prediction of het With multiple liner regression model we found tht distnce to cost, elevtion nd ltitude explined 74.6% (R 2 ) of vrince in the bsolute het mesure (bsed on temperture exceeding 40 C) (Tble 1). All three predictors hd expected signs of correltion, the tolernce rtes for collinerity were ll greter thn 0.85, nd the Vrince Infltion Fcts (VIF) were ll less thn Absolute het ws then predicted to the census trct level using the model from Tble 1. Bsed on Fig. 2 c, the bsolute het in the three counties generlly increses when moving inlnd from costl res. The reltive het mesures (bsed on temperture exceeding locl historicl norml mximum temperture) from the wether monitoring sttions were projected to the census trcts in the three counties by n inverse distnce weighting function. The reltive het mpped in Fig. 3 c demonstrtes tht these es re usully higher in plces closer to the costl res nd in low elevtion regions Descriptive sttistics For neighborhood rcil-ethnic composition, the census trct with the highest percentge of non-white popultion ws in Los Angeles County with 99.6% (Tble 2). The census trct with the Tble 1 Prediction of dily bsolute temperture exceednces for the Stte of Cliforni using 123 meteorologicl monitoring sttions. Predictor Coefficients t Sig. Collinerity sttistics B Std. error Tolernce VIF Constnt Ltitude (degrees) Elevtion (m) Distnce to costl line (km) VIF=vrince infltion fctor.

4 82 J.G. Su et l. / Environment Interntionl 40 (2012) Fig. 2. Predicted bsolute het for the counties of Almed (), Sn Diego (b) nd Los Angeles (c). smllest non-white popultion hd 0% non-whites, lso found in Los Angeles. Both Los Angeles nd Almed counties hd more thn 50% non-white popultions (51.3% for Los Angeles nd 51.5% for Almed), but Los Angeles hd the highest regionl vrition with stndrd devition of 28.7%. Los Angeles hd the gretest mximum (47.8 ppb) nd men NO 2 concentrtions (22.3 ppb) compred to Almed County (mximum ppb nd men 15.2 ppb) nd Sn Diego County (mximum nd men 13.5 ppb). The stndrd devition ws the smllest in Almed (3.1 ppb). Similr to NO 2, the men modeled concentrtions of PM 2.5 nd diesel PM were highest in Los Angeles County. Compred to Sn Diego County, Almed County hd lower men modeled concentrtions of PM 2.5 nd diesel PM. Los Angeles County hd the gretest mximum bsolute temperture exceednces (7.44 C per dy) but Sn Diego County hd the gretest men bsolute temperture exceednces (5.18 C per dy). Sn Diego County hd the gretest minimum bsolute temperture exceednces (4.46 C per dy) but the smllest stndrd devition (0.38 C per dy) mong the three counties. By contrst, Almed County hd the lowest mximum (2.97 C per dy) nd men (1.47 C per dy) bsolute temperture exceednces, lthough the stndrd devition (0.42 C per dy) ws higher compred to tht of Sn Diego. For reltive het, Sn Diego County hd the highest mximum (3.64 C per dy) reltive temperture exceednces. Almed County, by contrst, hd the gretest men reltive temperture exceednces (1.57 C per dy). Los Angeles County hd the lowest men reltive temperture exceednces (1.14 C per dy) nd its mximum reltive temperture exceednces (2.11 C per dy) ws between those of Almed County nd Sn Diego County. Overll, the degree of reltive het ws lower compred to tht of bsolute het Differences in inequlity between NO 2,PM 2.5, diesel PM, het, nd their cumultive burdens within counties Inequlity curves for ech of the three ir pollutnt estimtes, the two het indictors, nd the cumultive burdens re displyed in Fig. 4 c. The corresponding inequlities in exposure to single nd cumultive environmentl burdens, including the 95% confidence intervls, re shown in Tble 3. Within county difference results re shown in Tbles 4 nd 5. In Almed county, mong the three ir pollutnts, the gretest inequlity existed for diesel PM (concentrtion index, referred s C= 0.128), followed by NO 2 pollution (C = 0.045) nd PM 2.5 (C=0.003) (Fig. 4 nd Tble 3). Although different in size, ll the

5 J.G. Su et l. / Environment Interntionl 40 (2012) Fig. 3. Predicted reltive het for the counties of Almed (), Sn Diego (b) nd Los Angeles (c). single indices were significntly different from equlity (Tble 3). The inequlity in cumultive exposure to three ir pollutnts (cumultive burdens A) ws greter thn for ny single pollutnt lone (C= 0.179). The negtive signs ssocited with the inequlities indicted tht neighborhoods with higher proportion of non-whites encounter greter exposure to these environmentl hzrds. For PM 2.5, however, neighborhoods with higher proportion of non- Whites were exposed to lower modeled levels of ir pollutnts. Becuse the inequlity from PM 2.5 ws reltively smll nd hd n opposite sign compred to inequlities from the other two ir pollutnts, the inequlity in cumultive exposure to the three environmentl hzrds ws only borderline significntly different from tht of diesel PM (t=1.845 nd p=0.066). All other inequlities in exposure to single pollutnts were significntly different from one nother nd from the inequlity in exposure to the three pollutnts (Tble 4). Tble 2 Descriptive sttistics for rce-ethnicity nd het t census trct level within counties. Mesures Almed Sn Diego Los Angeles Min Men Mx Std Min Men Mx Std Min Men Mx Std % of popultion tht is nonwhite % of popultion tht is Hispnic % of popultion tht is Blck % of popultion tht is Asin Trffic-relted ir pollution (NO 2 ) (ppb) Air pollution fine prticultes (PM 2.5 )(μg m 3 ) Air pollution diesel PM (μg m 3 ) Absolute het ( C per dy) Reltive het ( C per dy) b Refers to men temperture exceednces for the dys when pprent tempertures were bove 40 C for the summer months (July, August nd September) in the periods. b Refers to men temperture exceednces for the dys when pprent tempertures were bove the locl historicl norml mximum temperture for the sme period s those used for clculting bsolute het.

6 84 J.G. Su et l. / Environment Interntionl 40 (2012) When het ws tken into ccount, we found (Fig. 4 nd Tble 3) tht het from bsolute temperture exceednces hd the second highest inequlity (C=0.062), only lower thn the inequlity from diesel PM; however, the het from reltive temperture exceednces hd the second lowest inequlity (C= 0.023), only slightly greter thn inequlity from PM 2.5. Furthermore, the inequlity from bsolute het took positive sign, indicting neighborhoods with greter proportion of Whites experienced higher bsolute temperture exceednces. By contrst, the inequlity from reltive het took negtive sign, indicting tht neighborhoods with greter proportion of non-whites experienced higher reltive temperture exceednces, lthough the level of inequlity ws smller thn tht of bsolute temperture exceednces. Bsedon the 95%confidence intervls in Tble 3, inequlities from both bsolute nd reltive het were significntly different (p b0.01) from the equlity line. In ddition, inequlities from both bsolute nd reltive het were significntly different compred to inequlities from other single nd cumultive environmentl burdens (pb0.01) (Fig. 5). Becuse of the opposite direction in inequlity for the bsolute nd reltive het es, the inequlities in exposure to the cumultive impcts from the three ir pollutnts nd the two het indices were not significntly different from those of the three pollutnts (t=0.646 nd p=0.519) (Tble 5). The reltive greter degree of inequlity from diesel PM mde it non-significnt compred to tht of the cumultive impcts from the three ir pollutnts nd the two het indices (t=1.107 nd p=0.269). Het nd other single nd cumultive burdens were significnt different from one nother (Tble 5). In Sn Diego, mong the three pollutnts, the gretest inequlity ws from diesel PM (C = 0.138), followed by NO 2 (C= 0.057) nd PM 2.5 (C= 0.004) (Fig. 4b ndtble 3). The cumultive inequlity in exposure to the three pollutnts ws greter thn for ny single pollutnts, nd the inequlity indices reflected tht neighborhoods with higher proportion of non-whites experienced greter environmentl hzrd burdens. Compring between the three environmentl hzrds nd the cumultive burdens, ll the inequlities were significntly different from one nother (p b0.05) (Tble 4). When het ws tken into considertion, we found tht the inequlity from bsolute het ws the smllest (C=0.002) mong ll the indices considered (Tble 3); however, the inequlity from reltive het ws the second highest (C= 0.116), only smller thn (but not sttisticlly significntly different from) tht of diesel PM (t=1.249 nd p=0.212) (Tble 5). The negtive sign nd the high degree of inequlity from reltive het indicted tht neighborhoods with higher proportion of non-whites experienced fr greter reltive temperture exceednces. By contrst, the bsolute het is nonsignificntly different from the equlity line bsed on the 95% confidence intervl in Tble 3 ( 0.001, 0.006). The ddition of het significntly incresed the inequlity from exposure to the three ir pollutnts (inequlity chnged from to 0.293). In Los Angeles county, lthough inequlity from diesel PM (C= 0.081) ws smller thn tht of Almed nd Sn Diego counties, mong the three environmentl hzrds considered, diesel PM exposure inequlity ws still the gretest in Los Angeles, followed by NO 2 ( 0.068) nd PM 2.5 ( 0.031) (Fig. 4c nd Tble 3). The inequlity in exposure to the three environmentl hzrds combined ws greter thn from ny single fctor. The inequlities hd the following chrcteristics: (1) they were significntly different from Fig. 4. Inequlities in exposure to NO 2,PM 2.5, diesel PM, bsolute nd reltive het es, nd the cumultive environmentl burdens from exposure to the three ir pollutnts (cumultive burdens A) nd from exposure to both ir pollution nd het (cumultive burdens B) in the counties of Almed (4), Sn Diego (4b) nd Los Angeles (4c).

7 J.G. Su et l. / Environment Interntionl 40 (2012) Tble 3 Rce-ethnicity inequlities in exposure to single nd cumultive environmentl hzrds from ir pollution nd het. Inequlity indictors Almed Sn Diego Los Angeles Ineq. index 95% CI Ineq. index 95% CI Ineq. index 95% CI NO ( 0.057, 0.035) ( 0.070, 0.044) ( 0.072, 0.063) PM (0.001, 0.004) ( 0.006, ) ( 0.034, 0.028) Diesel PM ( 0.161, 0.095) ( 0.167, 0.110) ( 0.091, 0.072) Cumultive burdens A ( 0.222, 0.136) ( 0.239, 0.159) ( 0.182, 0.159) Absolute het (0.042, 0.082) ( 0.001, 0.006) ( 0.015, 0.009) Reltive het ( 0.034, 0.011) ( 0.136, 0.097) (0.009, 0.024) Cumultive burdens B ( 0.202, 0.115) ( 0.337, 0.249) ( 0.183, 0.159) Cumultive burdens A refers to the inequlity of cumultive burdens from NO 2,PM 2.5 nd diesel PM. Cumultive burdens B refers to the inequlity of cumultive burdens from NO 2,PM 2.5, diesel PM, bsolute nd reltive het es. equlity; (2) they demonstrted tht neighborhoods with higher proportions of non-whites experienced greter environmentl hzrd burdens; nd (3) they were significntly different from one nother (Tble 4). When het ws considered, it showed tht inequlities from the bsolute nd reltive het es were in close rnge except for the direction of influence ( vs ). The effect of hving opposite signs on the inequlity index ment tht the two het indices cnceled ech other out in the cumultive inequlity from het. The combined het thus did not contribute significntly to the overll inequlity for the county: the difference between the cumultive burden inequlities with nd without the ddition of het hd t vlue of ndpvlueof0.991(tble 5) Differences in inequlity from exposure to ir pollution, het, nd their cumultive burdens between counties The degree of inequlity in ir pollution hzrd decresed from diesel PM to NO 2 nd to PM 2.5 for ll the three counties (Fig. 3 nd Tble 3). Significnt differences in inequlity existed, however, between the three counties for PM 2.5 exposure (pb0.05, see Tble 6). For NO 2, significnt difference ws seen between Almed nd Los Angeles counties (p=0.001) nd mrginlly non-significnt difference between Sn Diego nd Los Angeles counties (p=0.083). By contrst, Almed nd Sn Diego counties were not significntly different in inequlity from NO 2 exposure (p=0.236). For diesel PM, significnt differences in inequlity were seen between Almed nd Los Angeles counties (pb0.001) nd between Sn Diego nd Los Angeles counties (pb0.001), but not between Almed nd Sn Diego counties (p=0.653). All inequlities from cumultive burden in the three counties were greter thn the corresponding single fctor inequlities; however, no sttisticlly significnt difference existed for the inequlities from cumultive burdens between ny two counties. Tble 4 Within-county differences in inequlity from exposure to NO 2,PM 2.5, diesel PM nd the cumultive impcts using t tests. County Inequlity indictors PM 2.5 Diesel PM Cumultive A (t) (p) (t) (p) (t) (p) Almed NO b b b0.001 PM b b0.001 Diesel PM Sn Diego NO b b b0.001 PM b b0.001 Diesel PM Los Angeles NO b b0.001 PM b b0.001 Diesel PM b0.001 Cumultive A refers to the inequlity of cumultive burdens from NO 2,PM 2.5 nd diesel PM. For bsolute nd reltive het es, the directions of inequlity were lwys opposite for given county. In Almed, the degree of inequlity from the bsolute het ws bout three times tht of its reltive het. In Los Angeles, the inequlity from bsolute het ws very close in vlue to tht of reltive het except for the direction of influence. In Sn Diego, reltive het creted the highest degree of inequlity for ll the het indices in the three counties (C = 0.116). The inequlity from bsolute het in Sn Diego, by contrst, is not significntly different from tht of the equlity line. The smll levels of inequlity from het in Almed nd Los Angeles counties ment tht the ddition of het did not chnge the cumultive reltionship these two counties hd for the ir pollution burdens. We clculted t vlue of nd p vlue of for the ir pollution burden inequlities between Almed nd Los Angeles; corresponding vlues becme nd when het indices were dded (Tble 6). Becuse of the high impct from reltive het in Sn Diego County, the ddition of het mde the inequlities in cumultive burdens significnt between Almed nd Sn Diego counties (t =3.866 nd pb0.001) nd between Sn Diego nd Los Angeles counties (t = nd p b0.001) (Tble 6). Tble 5 Identifiction of within-county differences in inequlity from exposure to the two het indices, the three ir pollutnts nd their cumultive impcts. Region Inequlity indictors Absolute het Reltive het Cumultive B (t) (p) (t) (p) (t) (p) Almed NO b b0.001 PM b b0.001 Diesel PM b b Cumultive A b b Absolute het b b0.001 Reltive het b0.001 Sn Diego NO b b b0.001 PM b b0.001 Diesel PM b Cumultive A b b Absolute het b b0.001 Reltive het b0.001 Los Angeles NO b b b0.001 PM b b b0.001 Diesel PM b b b0.001 Cumultive A b b Absolute het b b0.001 Reltive het b0.001 Cumultive A refers to the inequlity of cumultive burdens from NO 2,PM 2.5 nd diesel PM. Cumultive B refers to the inequlity of cumultive burdens from NO 2, PM 2.5, diesel PM, bsolute nd reltive het es.

8 86 J.G. Su et l. / Environment Interntionl 40 (2012) Tble 6 Differences in inequlities in exposure to single nd cumultive environmentl hzrds between Almed, Sn Diego nd Los Angeles counties using t tests. Inequlity indictors Almed vs Sn Diego 4. Discussion nd conclusion Almed vs Los Angeles Sn Diego vs Los Angeles (t) (p) (t) (p) (t) (p) NO PM b b0.001 Diesel PM b b0.001 Cumultive burdens A Absolute het b b b0.001 Reltive het b b b0.001 Cumultive burdens B b b0.001 Cumultive burdens A refers to the cumultive burdens from NO 2,PM 2.5 nd diesel PM. Cumultive burdens B refers to the cumultive burdens from NO 2,PM 2.5, diesel PM, bsolute nd reltive het es. In this study, inequlities by rcil-ethnic composition from exposure to NO 2,PM 2.5, diesel PM nd their cumultive impcts were compred both within nd between counties. Additionlly, burdens from bsolute nd reltive het es were dded to these models. We treted bsolute het s experiencing extreme tempertures bove 40 C nd reltive het s hving extreme tempertures bove locl historicl norml mximum tempertures. Overll, our reserch corrobortes other studies showing tht communities of color ber greter environmentl pollutnts thn predominntly White nd more ffluent communities (Mrshll, 2008; Su et l., 2009c). Absolute het, expressed in the men per dy temperture exceednces in excess of 40 C, by contrst, followed the opposite direction in Almed County: communities of color experienced less bsolute extreme temperture exceednces. This might reflect tht the White popultion in Almed County minly lived in the estern suburbs further from the cost, leding to higher tempertures. Sn Diego County, however, did not hve significnt inequlity bsed on bsolute het. In Los Angeles, lthough bsolute het ws reltively smll in scle, communities with higher proportions of non-white residents experienced greter levels of this type of het. This is probbly becuse those disdvntged groups tend to reside further inlnd, especilly in Est Los Angeles. Reltive het inequlities were mong the smllest in the indices considered in this study for both Almed nd Los Angeles counties, indicting tht reltive het lone might not be the only significnt fctor resulting in dverse environmentl burdens for these two counties. Absolute het in Los Angeles County is lso reltively smll fctor. In Sn Diego, however, we sw tht neighborhoods with greter percentge of non-whites experienced fr greter inequlity in reltive temperture exceednces. Inequlities from bsolute het lwys took n opposite sign compred to those of reltive het for given county. These cnceling effects in het inequlity, however, were not the sme in the three counties. In Sn Diego County, the reltive het inequlity ws the highest, nd bsolute het did not contribute significntly to the overll het inequlity. In Los Angeles County, the two het indices cnceled ech other out. The cnceling effect in Almed County ws between Sn Diego County (no cnceltion) nd Los Angeles County (lmost totl cnceltion), due to the reltively greter bsolute het inequlity (compred to its reltive het inequlity). In modeling het, we used the meteorologicl dt from monitoring sttions. Generlly wether sttions re instlled high enough to void wind obstruction nd reduce impct from ground fetures. Our modeling process therefore did not tke into considertion ny ground fetures tht might mitigte het such s the presence of vegettion. Urbn vegettion my provide shde, moderte temperture nd help reduce het-relted illness for city dwellers (Blum et l., 1998; Cummins nd Jckson, 2001; Nowk nd Dwyer, 1997; Nowk et l., 1998). Lnd surfce temperture is mesurement of how hot the lnd is to the touch. It differs from ir temperture becuse lnd hets nd cools more quickly thn ir. Lnd surfce temperture should be more ccurte in mesuring ctul het felt by people on the ground. Future ttempts to quntify het will incorporte surfce temperture derived from remote sensing dt to detect smll re vrition of temperture. Het is lso relted to housing chrcteristics nd ir conditioning (Kovts nd Hjt, 2008). Air conditioning ws found to be n importnt protective fctor for het relted mortlity (Semenzet l., 1996). Subsequent studies should lso tke these household vribles into ccount given the vilbility of such dt. For the ir pollutnts NO 2,PM 2.5, nd diesel PM, the inequlities within county were significntly different in ll three regions. The inequlities in cumultive environmentl burdens with nd without the impct of het were lso significntly different from those of single burdens, mening there re corresponding significnt differences in sptil distribution. Becuse of the reltively smll effect from het, the difference in cumultive inequlities with nd without the burden from het ws not significnt, mening the ddition of het did not substntilly influence the sptil pttern of the inequlity. In this wy, the inequlity indices could be used to identify if significnt sptil difference or correltion exists when combining multiple environmentl burdens into one single index. When we clculted the series of inequlity indices using poverty sttus insted of rcil-ethnic sttus, the results were similr (see Supplementry mterils). This is not surprising becuse t the census trct scle, poverty nd non-white were highly correlted (r =0.69 in Almed County nd 0.77 in Los Angeles County). In Sn Diego County, by contrst, the inequlity in exposure to reltive het ws 0.044, much smller thn tht of rcil-ethnic grdient (C= 0.12). This demonstrtes tht though low income communities experienced greter exposure to reltive het, it ws the communities of color hd the gretest unfir exposure to this het in Sn Diego County. The single nd cumultive environmentl hzrd inequlity indices represent the reltive degree of inequlity in region. Higher sptil vrition of pollutnt would normlly result in greter inequlity index, while the homogeneity of n environmentl hzrd in sptil distribution would crete n index not significntly different from equlity. If levels of environmentl hzrds in region re lrgely bove the Ntionl Ambient Air Qulity Stndrd or bove known benchmrk, normliztion by benchmrk stndrd or other techniques should be used to identify those hot spots. Specil ttention should lso be pid to res with very few environmentl hzrds s well s res tht lck prticulr environmentl hzrd while other environmentl hzrd levels re high. The synergistic pproch pplied in our pper my indvertently indicte tht the cumultive impcts in such n re re lower, which in fct my not be the cse. In our nlysis, ny pollutnt within census trct with z-score greter thn 5 ws removed. We lso tested scenrios where no outliers were removed, nd we did not see ny lrge chnges in inequlity indices, but the curves were less smooth. For exmple, in Sn Diego, when ll the census trcts were included we identified tht the 10% of census trcts with the highest proportion of Whites hd greter levels of estimted exposure to diesel PM (the curve lies under the equlity line). After further investigtion, we found tht those communities were living on Corondo Islnd. Becuse of their proximity to the Sn Diego ports, Nvl Complex, nd Sn Diego Interntionl Airport, these communities hd higher diesel PM exposure. This inequlity, lthough, did not chnge the overll pttern tht neighborhoods of predominntly non-whites hd higher inequlity in environmentl burdens.

9 J.G. Su et l. / Environment Interntionl 40 (2012) Our reserch did not further clssify minority popultion into Africn Americn, Hispnic, Asin, nd other rce-ethnicity groups. These individul groups might hve different degrees of inequlity compred to the overll non-white group; however, the techniques used here provide wy to ssess rce-ethnicity nd poverty inequlities nd ssist decision mkers in prioritizing efforts to ddress inequlity issues. Trditionlly, epidemiologicl nd environmentl studies hve focused on single environmentl indices to exmine the mrginl ssocitions between single burdens nd helth outcome. In relity, these burdens coexist nd they might interct to synergisticlly worsen helth sttus. Evidence for multiplictive burdens (Environmentl Protection Agency, 2006; Muderly nd Smet, 2009) mkes ssessing single environmentl or socil fctors problemtic. Similrly, inequlities in exposure to multiple significnt environmentl hzrds should lso be pplied to identify the cumultive burden inequlity region might experience. In Almed County, census trcts with high proportion of White residents experiencing greter het thn predominntly non-white census trcts, but when influences from multiple burdens were tken into considertion, it ws the neighborhoods of non-whites tht hd greter cumultive environmentl burdens. Our reserch shows tht inequlities from multiple environmentl hzrds were generlly significntly different nd greter thn single hzrd inequlities. This lso demonstrtes the importnce of tking into ccount the cumultive burdens in ssessing inequlities of region for policy mking. Overll, our index llows for nlysis of the environmentl inequlity from multiple hzrd exposures. Although scientific evidence on the functionl form of cumultive effects remins formtive, the frmework provides screening ssessment tht incorportes cumultive burdens nd socil dt into one indictor. Focusing on cumultive burdens my led to policies tht directly trget communities of concern nd consequently led to improvements in public helth. Figures A1, A2 nd A3 demonstrte the inequlities in exposure to the three environmentl hzrds (NO 2,PM 2.5 nd diesel PM), the two het mesures nd their cumultive impcts bsed on poverty grdient. Supplementry mterils relted to this rticle cn be found online t: doi: /j.envint Acknowledgement We thnk Dr. Brt Ostro from the Office of Environmentl Helth Hzrd Assessment, Cliforni Environmentl Protection Agency nd Dr. Pul English from Cliforni Deprtment of Public Helth for providing us the NO 2 surfce for Almed County. We re lso thnkful to Dr. Bete Ritz, Dr. Michelle Wilhelm nd JoKy Ghosh for setting up the NOx smpling network nd for collecting smples. 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