Environmental Research 129 (2014) 39–46

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Air pollution and hospital emergency room and admissions for cardiovascular and respiratory diseases in Doña Ana County, New Mexico Sophia Rodopoulou a, Marie-Cecile Chalbot b, Evangelia Samoli a, David W. DuBois c, Bruce D. San Filippo d, Ilias G. Kavouras b,n a

Department of Hygiene, Epidemiology and Medical Statistics, Medical School, National and Kapodistrian University of Athens, Goudi, 115 27 Athens, Greece Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences, College of Public Health, 4301 West Markham St., Little Rock, AR 72205-7199, USA c Department of Plant and Environmental Sciences, New Mexico State University, Box 30003 MSC 3Q, Las Cruces, NM 88003-8003, USA d Memorial Medical Center, Las Cruces, NM 88003-8003, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 28 June 2013 Received in revised form 12 December 2013 Accepted 17 December 2013 Available online 15 January 2014

Introduction: Doña Ana County in New Mexico regularly experiences severe air pollution episodes associated with windblown dust and fires. Residents of Hispanic/Latino origin constitute the largest population group in the region. We investigated the associations of ambient particulate matter and ozone with hospital emergency room and admissions for respiratory and cardiovascular visits in adults. Methods: We used trajectories regression analysis to determine the local and regional components of particle mass and ozone. We applied Poisson generalized models to analyze hospital emergency room visits and admissions adjusted for pollutant levels, humidity, temperature and temporal and seasonal effects. Results: We found that the sources within 500 km of the study area accounted for most of particle mass and ozone concentrations. Sources in Southeast Texas, Baja California and Southwest US were the most important regional contributors. Increases of cardiovascular emergency room visits were estimated for PM10 (3.1% (95% CI:
0.5 to 6.8)) and PM10
2.5 (2.8% (95% CI:
0.2 to 5.9)) for all adults during the warm period (April–September). When high PM10 (4150 μg/m3) mass concentrations were excluded, strong effects for respiratory emergency room visits for both PM10 (3.2% (95% CI: 0.5–6.0)) and PM2.5 (5.2% (95% CI:
0.5 to 11.3)) were computed. Conclusions: Our analysis indicated effects of PM10, PM2.5 and O3 on emergency room visits during the April– September period in a region impacted by windblown dust and wildfires. & 2013 Elsevier Inc. All rights reserved.

Keywords: Particulate matter Morbidity Hospital admissions Emergency room visits Time-series regression Rural communities

1. Introduction Exposures to particulate matter (PM10 and PM2.5: particles with aerodynamic diameter less than 10 μm and 2.5 μm, respectively) and ozone (O3) are associated with increased mortality and morbidity, but also contribute to the development and progression of asthma and lung cancer (Ostro et al., 2006; Peel et al., 2005; Dominici et al., 2006; Zanobetti and Schwartz, 2005). The vast majority of epidemiological studies have been done in densely populated urban areas, where traffic and other anthropogenic sources were predominant (Peel et al., 2005; Samet et al., 2000;

Abbreviations: CI, Confidence interval; PM10, particles with diameter less than 10 μm; PM2.5, particles with diameter less than 2.5 μm; PM10
2.5, particles with diameter between 2.5 and 10 μm; O3, Ozone n Correspondence to: Department of Environmental and Occupational Health, University of Arkansas for Medical Sciences College of Public Health, 4301 W. Markham St., Slot #820, Little Rock, AR 72205-7199 USA. E-mail address: [email protected] (I.G. Kavouras). 0013-9351/$ - see front matter & 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.envres.2013.12.006

Katsouyanni et al., 2001; Mar et al., 2000; Staniswalis et al., 2005). Due to a limited power to detect effects, only a few epidemiological studies have focused upon smaller cities and rural areas impacted by a mixture of particle sources that experience intense air pollution episodes, have a diverse demographic profile and have limited access to health care facilities as compared to urban areas (Slaughter et al., 2005; Peng et al., 2008; Ulirsch et al., 2007; Perry et al., 2008; Brehm and Celedon, 2008; Tu et al., 2012). Doña Ana County is located in South-Central New Mexico and encompasses the city of Las Cruces. It borders the bi-national (i.e. extends into the US and Mexico) El Paso-Ciudad Juarez metropolitan area. Its population in 2010 was 209,234, with 65.9% having Hispanic/Latino origin. Comparatively, Hispanics and Latinos accounted for 46.7% and 16.7% of the population in New Mexico and the US. Exposures to particulate matter may have different effects depending on race and socioeconomic status. There are only a few studies addressing racial disparities (Grineski et al., 2007; Marshall, 2008). Since 1991, high PM10 levels have regularly been measured throughout the county with a PM10 non-attainment area

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S. Rodopoulou et al. / Environmental Research 129 (2014) 39–46

in Anthony, NM (EPA). Episodes of high PM10 and PM2.5 levels in the region were associated with high winds in spring and low winds in winter. PM10
2.5 particles of geological origin dominated PM10 mass and accounted for 25% of PM2.5 (Li et al., 2001). The PM2.5-to-PM10 ratio for low wind speed particulate matter pollution events for sites in New Mexico was 0.11 as compared to 0.22 for sites in El Paso, indicating the possible influence of emissions from unpaved roads (DuBois et al., 2010). The southern part of Doña Ana County was also designated as a maintenance area (i.e. exceeded the air quality standard (annual 4th highest daily maximum 8-h O3 concentration averaged over a 3-year period of 75 ppbv in the past, but it currently meets the standard due to actions taken to reduce emissions)) for O3 in 2004 and existing daily levels are frequently above 70 ppbv. Time series analysis of monthly 8-h max O3 levels showed declining trends from
0.07 to
1.28 ppbv/year; however, the declining rates are lower than that anticipated based on the reductions of O3 precursors from mobile and point sources. These discrepancies were attributed to spatiotemporal changes of nitrogen oxides and volatile organic compounds emissions, with wildfire being the most important factor on a regional scale (Jaffe and Widger, 2012). During the study period, Chalbot et al. (2013a) determined that wildfires may contribute up to 13 ppbv on daily 8-hour maximum O3 concentrations. Effects have previously been identified for childhood respiratory hospitalizations for both low- and high-wind particulate pollution events in El Paso, TX (Grineski et al., 2011). In this study, we attempted to evaluate the associations between short-term exposures to ambient PM10, PM2.5 and O3 levels and respiratory and cardiovascular emergency room visits and hospitalizations in Doña Ana County, an area with unique demographics and air pollution characteristics. The effect of particles and O3 from regional sources was also evaluated by estimating the contributions of sources located far away from the study area. 2. Methods 2.1. Air pollution and health data The study was approved by the Institutional Review Boards of the Memorial Medical Center (April 27, 2011) and the University of Arkansas for Medical Sciences (January 11, 2012). PM10, PM2.5 and O3 measurements in Doña Ana County during 2007–2010 were retrieved from the US. Environmental Protection Agency Air Quality System. The three sites were operated by the New Mexico Environment Department and were selected based on the completeness of the datasets (more than 99% for PM10 and O3 and 83% for PM2.5). The 24-h PM10 and PM2.5 mass concentrations and the daily 8-h maximum O3 concentration were used as metrics of exposures. PM10
2.5 concentrations were estimated as the difference between PM10 and PM2.5. Fig. 1 shows the population in Las Cruces and the locations of the monitoring sites and Memorial Medical Center. Daily emergency room visits and hospital admissions from the Memorial Medical Center in Las Cruces, New Mexico during the period from 2007 to 2010 for the adult population ( Z 18 years of age) were retrieved. The Memorial Medical Center is the largest medical center in Las Cruces with 67.5% of hospital beds (293) and 65% (35,939) of emergency room visits in Doña Ana County. The date of visit, type (emergency room visit or hospital admission), age, gender and race/ethnicity for respiratory (International Classification of Disease, revision 9 (ICD-9) (ICD-9 493, 466, 490, 491, 492, 496, 480–486, 460–465) and cardiovascular diseases ((CVD) codes 410–414, 426–427, 402, 428, 390–459) were retrieved.

Fig. 1. Population counts and the locations of air pollution monitoring sites and the Memorial Medical Center in Las Cruces, New Mexico. from the receptor site. They may disproportionally influence days with high (or low) concentrations, but the error is reduced to less than 10% for increasing averaging periods (Poirot and Wishinski, 1986; Gebhart et al., 2006, 2011; Chalbot et al., 2013a). In our application, the contributions of source regions on particle mass and O3 in Las Cruces were determined by regressing 24-h mean PM10 and PM2.5 mass concentrations and daily 8-h maximum O3 concentrations (Ci) against the residence time of air mass in each region (tj, in hrs) on i-day. 19

C i ¼ α þ ∑ t ij βj j¼1

ð1Þ

The source contributions were computed by running the regression model without the intercept. Since all cells in which the air masses spent at least 72 h over the study period were included, contributions from sources outside the regions described above may be negligible (Chalbot et al., 2013b; Xu et al., 2006). The locations of air masses (i.e. trajectory point) every hour for the past 5 days, prior to their arrival at the receptor site, were computed using National Oceanic and Atmospheric Administration0 s Hybrid Single-Particle Lagrangian Integrated Trajectory model. Trajectories were computed every fourth hour beginning at 00:00 coordinated universal time and at a starting height of 500 m which was within the planetary boundary layer (Seidel et al., 2012) using the Eta Data Assimilation System meteorological datasets (Kavouras et al., 2009). The residence time for each 1o  1o cell was calculated as the sum of the number of trajectories points within the cell (Poirot and Wishinski, 1986; Kavouras et al., 2013; Chalbot et al., 2013b). The domain of the source regions for the regression analysis included all cells with residence time of at least 72 h over the study period. A total of nineteen source regions were defined including four regions around Las Cruces, New Mexico with 51  51 dimensions (Fig. 2). The source regions were: (1) Pacific Ocean off the US coast; (2) Pacific Northwest and northern/central California; (3) western Rockies including northern Nevada; (4) northern Rockies and Plains; (5) Upper Midwest; (6) Central Lakes; (7) southern California, Nevada and western Arizona; (8) western New Mexico and eastern Arizona; (9) eastern New Mexico; (10) southwest New Mexico and adjacent areas in Mexico; (11) southwest Texas and Ciudad Juarez in Mexico; (12) southern Plains; (13) south Midwest; (14) Pacific Ocean off the Baja California coast; (15) Baja California; (16) southwest Mexico; (17) southeast Texas and Mexico; (18) Gulf of Mexico and southern US; and (19) southern Mexico. The outcomes of this analysis afforded us the ability to determine the effects of particulate matter and ozone from regional sources as compared to those associated with local sources. This is important because it enables local air quality agencies and communities to determine the spatial characteristics of air pollutionresulted health effects and implement controls to reduce the overall burden.

2.2. Trajectory regression analysis 2.3. Time series analysis Regional transport of pollutants released from sources far away from the receptor sites (and occasionally outside the jurisdiction of local air quality agencies) has been recognized as a significant determinant of air pollution in the US (Kavouras et al., 2009; Chalbot et al., 2013a). To estimate the influence of regional sources on particle mass and ozone concentrations in Las Cruces, we applied the Tracer Mass Balance model, initially developed by Pitchford and Pitchford (1985). The accuracy, sensitivity and reliability of the Tracer Mass Balance model were validated by releasing perfluorocarbon tracers from source regions and monitoring their levels in receptor sites (Green et al., 2003). Limitations of the approach are associated with nonrandom errors in trajectories locations, unstable estimates of daily concentrations and inability to estimate the contributions from sources within a few kilometers

The pollutant-health outcomes associations were investigated using Poisson regression models allowing for overdispersion (McCullagh and Nedler, 1989; Katsouyanni et al., 2009), expressed as: log E ½Y t  ¼ β0 þ b  Pollutantt þ s ðtimet ; kÞ þ s ðtempt ; kÞ þ s ðlag16 ðtempt Þ; kÞþ ½others

ð2Þ

where E[Yt] is the expected value of the Poisson distributed variable Yt indicating the daily visits or admissions count on day t with Var(Yt)¼ φE[Yt] and φ being the over-dispersion parameter; tempt is the value of mean temperature on day t; lag16

S. Rodopoulou et al. / Environmental Research 129 (2014) 39–46

41

inclusion of influenza control. The levels of the pollutant under investigation on the previous day (lag 1) of the visit or admission were included in the model. In order to investigate effect modification patterns by season, we also ran separate analyses for the cold (October–March) and hot (April–September) period. We repeated the analysis regarding particles effects after excluding days with concentrations above the 99th percentile of their distribution, which corresponded to days above 150 μg/m3 for PM10 (8 days excluded), above 41 μg/m3 for PM2.5 (9 days removed) and above 131 μg/m3 for PM10
2.5 (8 days removed). We used R statistical package for the analysis (version 2.15.0, Vienna, Austria). Finally, we evaluated the sensitivity of our results by applying different approaches for seasonality control, namely 9 df per year for the splines smoother in equation or by fitting a case-crossover approach for seasonality control by modeling the time trend in the Poisson models with a three-way interaction between year, month and day of death (Lu et al., 2008). We also evaluated the effects of the two days average exposure of pollutants (lags 0
1) to the analyzed outcomes.

3. Results 3.1. Study population and air pollution characteristics The total number of emergency room visits for adults (all ages) for respiratory and cardiovascular were 4739 and 2031 in 2007– 2011 (daily mean of 3 and 1, respectively) with 1% and 46% of them

Fig. 2. The source regions.

Table 1 Daily hospital emergency room visits and admissions and PM10, PM2.5 and O3 levels in Doña Ana County. N

Emergency room visits Respiratory All ages þ65 years Cardiovascular All ages þ65 years Hospital admissions Respiratory All ages þ6 years Cardiovascular All ages þ65 years

Mean (min–max)

Median (25th–75th percentiles)

4739 599

3.2 (0–13) 0.4 (0–4)

3 (2–4) 0 (0–1)

2031 941

1.4 (0–7) 0.6 (0–4)

1 (0–2) 0 (0–1)

2381 1382

1.6 (0–9) 0.9 (0–7)

1 (1–2) 1 (0–2)

5161 3115

3.5 (0–12) 2.1 (0–9)

3 (2–5) 2 (1–3)

Meteorology Wind speed (m/s) Ambient Temperature (1C) Relative humidity (%) Hourly precipitation (mm H2O) Pressure (mbar)

7.2 17.2 40.4 1.4 865

(0–48) (
9–57.2) (4–100) (0.3–26.9) (845–883)

Pollutants 24-h PM10 (μg/m3) 24-h PM2.5 (μg/m3) 24-h PM10
2.5 (μg/m3) Max 8 h-O3 (ppbv)

20.3 10.9 9.4 43.2

(2.5–416.7) (0.4–55.6) (0.5–368.5) (0–70)

6 17.8 37 0.5 865 16 9 7 43

Table 2 Correlation coefficient matrix (Pearson) of air pollution and meteorological variables.

8 h max O3 1 h max O3 24 h O3 PM10 PM2.5 Temperature

1 h max O3

24 h O3

PM10

PM2.5

Temperature

Relative humidity

0.95 1

0.87 0.80 1

0.18 0.19 0.24 1


0.05 0.03
0.04 0.41 1

0.59 0.58 0.58 0.16
0.14 1


0.37
0.34
0.27
0.27
0.32
0.18

(3–9) (9–24) (24–54) (0.3–1.5) (862–867) (11–23) (6–13) (3–12) (36–51)

(tempt) is the temperature lagged effect over the previous six days; and (Pollutant)t is the pollutant0 s level on day t. The smooth functions (s) captured the non-linear relationship between the timevarying covariates and calendar time and daily visits or admissions. We used natural splines as smoothing functions. The smooth function of time serves as a proxy for any time-dependent outcome predictors or confounders with long-term trends and seasonal patterns not explicitly included in the model (Touloumi et al., 2004). Hence, we removed long-term trends and seasonal patterns from the data to guard against confounding by omitted variables. We controlled for season and long-term trend with a natural cubic regression spline with 1.5 degrees of freedom (df) for each season and year (corresponding to 6 df per year). This amount of seasonality control has been previously suggested for the analysis of hospital admission data and eliminates confounding effects from seasonal and longer-term trends but retains shorter-term fluctuations, part of which may be causally associated with short-term fluctuations in the pollutants (Zanobetti and Schwartz, 2009). We also included natural splines with three df for temperature on the day of the admission and with 2 df for the six previous days and a linear term for daily average humidity and dummy variables for the day of the week effect and public holidays. We also controlled for influenza outbreaks in the analysis of cardiovascular effects and tested the sensitivity of the respiratory effect estimates to the

Fig. 3. Daily total hospital emergency room visits (a) and admissions (b), and max 8-h O3 (c), 24-h PM10 (d) and 24-h PM2.5 and (e) levels in Dona Ana County.

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S. Rodopoulou et al. / Environmental Research 129 (2014) 39–46

for elderly (65þ years) adults (Table 1). A total of 2381 and 5161 (daily mean of 2 and 4, respectively) respiratory and cardiovascular hospitalizations were reported for the same period, most of them (58–60%) for adults older than 65 years. The mean 24-h PM10 and PM2.5 concentrations were 20.3 μg/m3 (from 2.5 to 416.7 μg/m3) and 10.9 μg/m3 (from 0.4 to 55.3 μg/m3) indicating that a large fraction of PM10 was associated with coarse particles. The mean daily 8-h maximum O3 concentration was 43.2 ppbv with a maximum concentration of 70 ppbv. The correlation between PM10 and PM2.5 was moderate (0.41 po0.001) (Table 2). Ambient temperature was also positively correlated with O3 (0.58, po0.001). Fig. 3 shows the variability of hospital emergency room visits and admission and air pollution parameters. Hospital emergency room visits presented greater variability than admissions. Increased counts were observed in winter and spring and somewhat lower counts in the summer. A similar pattern was observed for PM10 and PM2.5, albeit with strong inter-annual variability. Higher particle mass concentrations were typically observed in February–April than the remaining months, with the most severe events in 2008. O3 concentrations reach their maxima in the summer months because of the strong dependence of ozone formation on solar radiation.

compared to those in winter and fall. Air masses in spring and summer originated either from Southwest US (Southern California and Arizona and Baja California) or along the Rio Grande River Valley (US–Mexico border) and Gulf of Mexico. Trajectories in fall and winter covered a smaller geographical area within a few

3.2. Local and regional contributions The normalized residence times of air masses arriving in Las Cruces at 500 m for winter (a), spring (b), summer (c) and fall (d) are illustrated in Fig. 4. There were clear differences between air masses arriving in Doña Ana County in spring and summer as

Fig. 5. The contributions of the source regions on PM10, PM2.5 and O3 concentrations.

Fig. 4. Normalized air mass residence time in 0.51  0.51 horizontal grid cells using backward trajectories at 500 m in winter (a), spring (b), summer (c) and fall (d).

S. Rodopoulou et al. / Environmental Research 129 (2014) 39–46

43

Table 3 Percent increase (and 95% confidence intervals (CIs)) in hospital emergency room visits and admissions for respiratory and cardiovascular outcomes associated with 10 μg/m3 increase of PM10, PM2.5 and PM10
2.5 and 10 ppbv increase in 8 h max O3 in previous day0 s concentration of the corresponding pollutant. Outcome

PM2.5

PM10

Emergency room visits Respiratory All ages 65þ years Cardiovascular All ages 65þ years

PM10
2.5

O3


0.1 (
1.6, 1.6) 1.7 (
1.5, 5.1)

3.0 (
2.1, 8.3) 1.2 (
11.3, 15.4)


0.3 (
2.1, 1.4) 1.6 (
2.0, 5.4)


3.3 (
8.3, 2.0) 9.8 (
4.4, 26.0)

1.7 (
0.3, 3.7) 2.2 (
0.3, 4.8)

4.5 (
3.2, 12.9) 8.3 (
3.0, 20.9)

1.5 (
0.7, 3.9) 1.9 (
1.2, 5.1)

2.93 (
4.4, 10.8) 8.06 (
2.8, 20.1)

Hospital admissions Respiratory All ages 65þ years Cardiovascular All ages 65þ years

0.8 (
1.0, 2.6) 1.7 (
0.4, 3.8)

1.3 (
5.0, 8.0)
1.71 (
9.7, 7.0)

0.8 (
1.1, 2.8) 1.8 (
0.5, 4.1)

3.9 (
3.1, 11.4) 7.1 (
2.5, 17.6)

0.4 (
1.0, 1.8) 0.6 (
1.2, 2.4)


1.4 (
6.2, 3.6)
1.6 (
7.7, 4.8)

0.2 (
1.4, 1.8) 0.6 (
1.4, 2.7)

1.6 (
3.0, 6.4) 1.0 (
4.8, 7.2)

Table 4 Effects of PM2.5, PM10
2.5, PM10 and O3 (lag1) for respiratory and CVD hospital emergencies room visits and admissions per season and excluding days with extreme particles0 concentrations. Morbidity

Outcome

Pollutant

Percent Increase (95% CI) Cold Period

Emergencies

Respiratory

Cardiovascular

Admissions

Respiratory

Cardiovascular

Hot days

PM2.5 PM10
2.5 PM10 O3 PM2.5 PM10
2.5 PM10 O3

1.8
0.7
0.4
5.4 2.4 0.3 0.6 2.6

(
4.3, 7.6) (
2.8, 1.4) (
2.3, 1.5) (
12.7, 2.6) (
6.4, 11.9) (
2.6, 3.3) (
2.0, 3.3) (
9.0, 15.7)

0.59
0.21
0.03 3.28 12.42 3.10 2.80 6.03

(
10.4, 13.0) (
3.5, 3.2) (
2.8, 2.8) (
3.8, 10.9) (
3.5, 30.9) (
0.5, 6.8) (
0.2, 5.9) (
3.7, 16.8)

PM2.5 PM10
2.5 PM10 O3 PM2.5 PM10
2.5 PM10 O3

4.1 1.1 1.3 6.5
1.3 0.5 0.7
0.8

(
3.0, 11.8) (
1.0, 3.2) (
0.6, 3.3) (
3.7, 17.9) (
7.0, 4.8) (
1.6, 2.6) (
1.2, 2.6) (
8.4, 7.4)


5.9
1.0
0.9 0.9
1.3
0.2
0.6 0.7

(
19.6, 10.1) (
5.1, 3.4) (
4.3, 2.6) (
8.6, 11.4) (
10.2, 8.6) (
2.8, 2.3) (
2.7, 1.6) (
4.9, 6.7)

hundred miles from the study area, extending into southern Arizona, southwest New Mexico and northern Mexico. Fig. 5 shows the mean (72 s) contributions of the nineteen source regions to PM10, PM2.5 and O3 concentrations in Doña Ana County. The four regions with the highest contributions on PM10 mass were Baja California (4.170.4 μg/m3), southwest US (2.870.3 μg/m3), southwest New Mexico (2.670.4 μg/m3) and Pacific Northwest (2.570.4 μg/m3). Sources in southwest New Mexico (1.970.2 μg/m3), Baja California (1.470.1 μg/m3) and Pacific Northwest (1.170.2 μg/m3) and southwest Texas (El Paso-Ciudad Juarez) (1.270.1 μg/m3) added approximately 58% of PM2.5 mass in Las Cruces. For O3, the four highest contributors were Baja California (6.470.4 ppbv), southwest Texas (5.070.5 ppbv), southeast Texas/Mexico (4.870.3 ppbv) and southwest New Mexico (4.870.7 ppbv). The four adjacent sectors to Las Cruces engulfing El Paso and Ciudad Juarez added, on average, 28% of PM10, 34% of PM2.5 and 34% of O3 concentrations. 3.3. Respiratory and cardiovascular effects Table 3 shows the percent increase in emergency room visits and hospital admissions for respiratory and cardiovascular diseases for all ages and 65 þ for an increase of 10 units in the corresponding pollutant. For respiratory emergency room visits, a positive increase was computed for all adults only for PM2.5 (3.0% (95% CI:
2.1 to 8.3)). For those above 65þ , we found indications

Excluding extreme PM concentrations 5.2 (
0.5, 11.3) 2.2 (
0.6, 5.1) 3.2 (0.5, 6.0) – 4.5 (
4.2, 14.1) 2.3 (
2.0, 6.9) 3.6 (
0.4, 7.7) – 0.8 (
6.4, 8.5) 1.4 (
2.2, 5.2) 0.8 (
2.6, 4.4) –
2.8 (
8.2, 2.8)
0.7 (
3.4, 2.1)
0.1 (
2.6, 2.5) –

of effects of all analyzed pollutants (PM10: 1.7% (95% CI:
1.5 to 5.1); PM2.5 1.2% (95% CI:
11.3 to 15.4); PM10
2.5: 1.6% (95% CI:
2.0 to 5.4) and O3: 9.8% (95% CI:
4.4 to 26.0)). For cardiovascular emergency room visits, our results indicate adverse associations for all ages and 65 þ years for all pollutants. Regarding hospital admissions, our estimates indicate effects of all pollutants, except in the association between PM2.5 and respiratory admission among 65þ adults where essentially the effect was null. The seasonal effects of particulate matter and O3 on respiratory and cardiovascular emergency room visits and hospitalizations for all ages and 65þ years are illustrated in Table 4. Effects of O3 were larger in the hot period, except for the case of respiratory admissions. Particles effects on cardiovascular emergency room visits were also higher in the warm period, (PM10 (2.8%, 95% CI:
0.2 to 5.9) and PM10
2.5 (3.1%, 95% CI:
0.5 to 6.8)). Similarly, when extreme PM10 mass concentrations were excluded, we observed stronger effects for respiratory emergency room visits for both PM10 (3.2% (95% CI: 0.5–6.0)) and PM2.5 (5.2% (95% CI:
0.5 to 11.3)). Our results were robust to alternative modeling approaches (i.e. applying the case crossover model or using 9 df for seasonality control), since effects were altered by less than 15% for the vast majority of these associations. Respiratory effects were also robust to the inclusion of influenza control in the models since, for

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S. Rodopoulou et al. / Environmental Research 129 (2014) 39–46

Table 5 Percent increase (and 95% confidence intervals (CIs)) in selected health outcomes per an interquartile increase in previous day0 s concentration of the corresponding pollution metric. Respiratory emergency room visits

Cardiovascular hospital admissions

PM10 regional source Central Pacific Ocean Pacific Northwest Southwest US Baja California Southeast Texas

1.7 2.1
1.2
1.9 0.8

(
1.0, 4.5) (
2.7, 7.2) (
4.7, 2.5) (
6.6, 3.0) (
0.9, 2.6)


0.4
1.3
1.2
0.9 0.6

(
3.0, (
5.6, (
4.3, (
5.2, (
0.8,

PM2.5 regional source Central Pacific Ocean Pacific Northwest Southwest US Baja California Southeast Texas

1.6 1.6 0.8
2.2 0.3

(
1.3, 4.6) (
3.5, 7.1) (
3.1, 4.7) (
7.1, 2.9) (
1.5,2.1)


1.9
1.5
0.2
1.1 0.7

(
4.6, 1.0) (
6.2, 3.5) (
3.6, 3.3) (
5.6, 3.6) (
0.8, 2.2)

O3 regional source Central Pacific Ocean Pacific Northwest Southwest US Baja California Southeast Texas

1.7 2.1
0.9
2.4 0.8

(
1.0, 4.5) (
2.7, 7.2) (
4.5, 2.8) (
7.0, 2.5) (
0.9, 2.6)


0.5
1.2
1.2
0.6 0.6

(0.5,
1.4) (1.2,
3.6) (1.2,
3.6) (0.6,
1.7) (
0.6, 1.8)

2.2) 3.3) 2.0) 3.4) 2.0)

example, the effect of PM2.5 on emergency room respiratory visits increased slightly to 3.3% from 3.0% (without control), and for hospital admissions, it increased to 1.5% from 1.3%. Finally, when considering two days exposure, effects were higher for emergency room visits for respiratory outcomes and PM2.5 exposure (6.57% increase, 95% CI:
0.11 to 13.70), a pattern that was not replicated in the respiratory hospital admissions. The effects of the regional contributions from five regions on PM10, PM2.5 and O3 on respiratory and cardiovascular emergency room visits and hospitalizations are presented in Table 5. We estimated consistent associations between both morbidity indicators and the three pollutants from southeast Texas and for the Pacific regional source with emergency room respiratory visits.

4. Discussion In this study, we identified and quantified the local and regional contributions to PM10, PM2.5 and O3. Sources within 500 km south (SW and SE New Mexico) of the study area contributed the most in particle mass and O3 concentrations. Most of PM10 and PM2.5 was of geologic origin with the PM10
2.5 fraction dominating (about 75%) the total PM10 mass (Li et al., 2001). Road dust from unpaved and paved roads was one of the most important sources of PM10 and PM2.5 in the study area. There are two unique types of high particulate pollution in the region. The first one is described by low wind air stagnation from December to February. They were attributed to emissions from unpaved roads (DuBois et al., 2010). The second is facilitated by strong winds resulting in the resuspension of loose soil particles into the air. These dust storms occur in mid-April from source regions within 500 km of the study area (Rivera et al., 2009). The regions with the three highest contributions of PM10, PM2.5 and O3 were Baja California, southwest US (southern Arizona and California), southeast Texas and Pacific Northwest. The seasonal variability of the air mass residence times indicated that the contributions from southwest US and Baja California may be more pronounced in winter and spring, while emissions from sources in southeast Texas may be mostly present in the summer. Owing to differences in source emissions, particles in southwest US were mostly composed of carbonaceous aerosol and dust particles

(Hand et al., 2012). In comparison, sulfate and nitrate were the primary components of particulate matter in Texas. The chemical composition of particles has been previously determined to modify cause-specific hospital admissions (Kim et al., 2012; Zanobetti et al., 2009). An initial assessment of the existing public health status indicators, using the Hospital Inpatient Discharge Data and the Behavioral Risk Factor Surveillance System (data obtained from the online New Mexico0 s Indicator-Based Information System query https://ibis.health.state.nm.us/home/Welcome.html) for the study area, showed that the asthma mortality rate in Doña Ana County (1.5 per 1,000,000) was 50% higher than the mortality rates in New Mexico and nationwide. Asthma hospitalization rates have increased by 0.2 per 10,000 per year from 1999 to 2010 with higher hospitalization rates being observed for females than for males (ages 18–44; 3.5–19.5 per 10,000 for females to 1.7–9.6 per 10,000 for males). For chronic obstructive pulmonary disease, the mortality rate was 41.5 per 1,000,000 (45.6 in New Mexico and 41.1 in the US) and hospital admissions rates have increased by 0.3 per 10,000 per year. Heart failure was the leading cause of mortality in Doña Ana County for ages 65 þ with a rate of 14.1 per 100,000. Heart-related hospital admissions decreased from 19.8 per 10,000 in 1999 to 16.6 per 10,000 in 2004 and remained unchanged since then. We estimated the effects of ambient PM10, PM2.5, PM10
2.5 and O3 on hospital emergency room visits and hospitalizations. There were twice as many emergency room visits for respiratory symptoms as compared to admissions for all adults; but the opposite was true for the elderly. For cardiovascular morbidity, admissions were up by 1.5 times over emergency room visits for all adults and three times for elderly. Substantial differences of the effects of air pollution have been previously observed using hospital emergency room visits and admissions attributed to differences in air pollution mix as well as demographic and socioeconomic characteristics and the type and severity of the symptoms (Winquist et al., 2012). We found effects of the PM10, PM10
2.5 and O3 with respiratory and cardiovascular hospital emergency room visits and admissions for adults 65 þ years, when we removed days with extreme concentrations. This indicated a logarithmic shape of the association under investigation. Access to health care and health insurance may also modify the effects. Based on the Behavioral Risk Factor Surveillance System data, only 70% of residents have health insurance coverage and approximately 20% were unable to get health care due to costs in Doña Ana County. The percent increases of hospital admissions for asthma during high and low wind PM2.5 events for people of all ages in El Paso, Texas were 11% (95% CI:
4 to 28) and 7% (95% CI:
4 to 21) (Grineski et al., 2011). El Paso is located approximately 70 km south of Las Cruces and, like Las Cruces, also experiences severe air pollution events associated with windblown dust. Low income individuals covered by Medicaid (patients under the age of 65 with a minimum of 133% of the federal poverty level ($29,700 for a family of four)) were more susceptible to both high and low wind pollution events (Grineski et al., 2011). According to the latest US Census, about 24% were living under the poverty level with an annual income of less than $16,000 per capita in Doña Ana. Spokane, a city in eastern Washington State, experienced severe air pollution problems, almost identical to those in Doña Ana County. It is currently characterized as a maintenance (previously non-attainment) area for PM10, with soil dust particle being a major component of particulate matter. In a previous study, the percent increases for respiratory emergency room visits for a 25 μg/m3 increase of lag1 PM10 and PM10
2.5 were 1% (95% CI:
1 to 4) and 1% (95% CI:
2 to 4) (Slaughter et al., 2005). That study found no association between respiratory hospital

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admissions and PM10, PM2.5 mass concentrations, but reported an indicative risk for cardiac admissions (PM10: 0% (95% CI:
4 to 3); PM2.5 2% (95% CI:
1 to 5)). We found an association between cardiovascular emergency room visits and PM10, PM10
2.5 and O3 during the warm period (April–September). This period included the high wind PM10 episodes and summertime O3 events. Higher effects of air pollution during the hotter period of the year have been identified earlier in similar settings (Samoli et al., 2008). We observed a strong effect of summertime O3 concentrations on cardiovascular emergency room visits among the elderly. We previously identified that, during the study period, wildfire incidents in southeast US, Cuba and central Mexico added from 5 up to 19 ppbv to 8-h maximum O3 concentration (Chalbot et al., 2013a). Similar effects of O3 on respiratory health were also recently observed in Reykjavik, Iceland, an urban setting with low PM10 levels and were attributed to a temporary increase of interleukin-1β, decrease of plasminogen activator inhibitor 1 and plasminogen and changes in heart rhythm (Devlin et al., 2012). Finally, we found indications of associations between respiratory emergency room visits or cardiovascular admissions with the contributions from southeast Texas for PM10, PM2.5 and O3. This region includes two coal-fired power plants (Carbón I and II with 1200 and 1400 MW capacities located approximately 32 km south of the US–Mexico border near the town of Eagle Pass, Texas), oil refining, oil-fired power production, steel production and other industrial operations (Pitchford et al., 2005). The Tampico region on the Gulf of Mexico has many oil refining and oil-fired power generation facilities. Previous studies found associations of nickel and vanadium, both tracers of diesel combustion, with cardiovascular diseases (Kim et al., 2012; Bell et al., 2011). There was no indication of an association between the three pollutants and cardiovascular admissions for the other four regions (central Pacific Ocean, Pacific Northwest, southwest US and Baja California). There were several limitations to this study. This includes the measurement error imposed by the use of air pollution measurements from fixed monitoring sites that may better represent ambient exposures of residents in Las Cruces than those living in rural areas. In addition, it was not possible to analyze the effects on specific diagnoses (such as asthma, stroke, and ischemic heart disease) due to limited power derived from the small population size of the study area. On the other hand, our study has several major strengths with the most important being the demographic characteristics of the study area, allowing for the characterization of air pollution health effects in Hispanics and Latinos. Using the trajectories regression analysis, the significance of local emissions as compared to regional sources was evaluated. The concurrent analysis of emergency room visits and hospital admissions allowed for the determination of the possible influence of access to health care facilities and health insurance, a key variable that may be undetected in metropolitan areas with many urgent and emergency care facilities.

5. Conclusions Overall, we investigated the effects of the particulate matter and O3 on respiratory and cardiovascular emergency room visits and hospital admissions in adults in a region influenced by mineral dust particles and wildfires. We, generally, found effects of air pollution on respiratory and cardiovascular morbidities, some of which became stronger when high PM10 concentrations were excluded. Regional transport from southeast Texas also showed positive association with both morbidity indicators. Reduced health insurance coverage and access to healthcare facilities may obscure the effects of air pollution on health in

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smaller cities and rural communities with large percentages of Hispanics and Latinos. Future research may include the investigation of air pollution effects on childhood emergency room and hospital admissions, since children are a vulnerable population subgroup and may spend more time outdoors. Early childhood is a critical period for the development and maturation of the brain, lung and immune system, and air pollution may impair lung function and initiate or contribute towards the development of asthma. Furthermore, the effects of specific types of events, such as dust storms and fire-related O3, may be examined.

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