Shrinking lakes, air pollution, and human health: Evidence from California's Salton Sea

Science of the Total Environment 712 (2020) 136490

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Shrinking lakes, air pollution, and human health: Evidence from California's Salton Sea Benjamin A. Jones a,⁎, John Fleck b a b

University of New Mexico, Albuquerque, NM, USA University of New Mexico, Water Resources Program, Albuquerque, NM, USA

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• Shrinking lakes are an increasing source of air pollution. • Mortality impacts due to PM2.5 from the shrinking Salton Sea are studied. • A two-stage instrumental variables method is used to address pollution endogeneity. • Salton Sea-induced PM2.5 is associated with increased respiratory mortality. • Illustrates need to balance water demand with public health impacts in arid areas.

a r t i c l e

i n f o

Article history: Received 19 October 2019 Received in revised form 30 December 2019 Accepted 31 December 2019 Available online 07 January 2020 Editor: Damia Barcelo

a b s t r a c t Due to increased water withdrawals and ongoing climate change, many inland lakes around the world are shrinking and dry lake beds can be significant sources of particulate matter air pollution. Using a natural experiment provided by the shrinking Salton Sea in California, this paper shows that each one-foot drop in lake elevation is associated with a 0.28 μg/m3 (2.6%) increase in PM2.5 concentrations. IV model results then show that Salton Sea-induced changes in PM2.5 over 1998–2014 led to increases in respiratory mortality of 1.4/yr.–15.6/ yr. in the counties surrounding the lake, generating $13.2–$147.3 million in annual health costs. © 2020 Elsevier B.V. All rights reserved.

Keywords: Inland lakes Air pollution Human health Salton Sea PM2.5 Satellite PM2.5 data

1. Introduction Human alterations to the hydrologic system, through diversion of water for agriculture and municipal use as well as via climate change, ⁎ Corresponding author at: Department of Economics, University of New Mexico, 1 UNM Drive, MSC 05 3060, Albuquerque, NM 87131, USA. E-mail address: [email protected] (B.A. Jones).

https://doi.org/10.1016/j.scitotenv.2019.136490 0048-9697/© 2020 Elsevier B.V. All rights reserved.

are reducing inflows to inland lakes around the world, causing them to shrink (Wurtsbaugh et al., 2017). Airborne pollutants from newly exposed shorelines have long been understood to create health risks, but those risks have been poorly quantified, leaving policymakers without the tools needed to evaluate tradeoffs as water management decisions are made. In particular, newly exposed lake bed can be a significant source of fugitive dust aerosol and particulate matter (PM) emissions (Rashki et al., 2013; Engelstaedter et al., 2006). Wind, weather, and

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other natural erosion processes cause dirt, salts, and other particulates found in dry lake beds to be transported into the atmosphere, thereby affecting local, regional, and even national air quality. For instance, according to the US EPA, the exposed lake bed from the nearly-dry Owens Lake in central California is the “largest single source” of PM in the US (US EPA, 2017). Daily PM10 emissions around Owens Lake can exceed 12,000 μg/m3; the Federal PM10 standard is 150 μg/m3 (Gillette et al., 2004).1 In light of the substantial evidence that PM is damaging to human health (e.g., Anderson et al., 2012; Pope III and Dockery, 2006; Chay and Greenstone, 2003), shrinking inland lakes are thus becoming a major public health concern (Johnston et al., 2019; Wurtsbaugh et al., 2017; Goudie, 2014; Taylor, 2002). Owens Lake is one of many such examples. Lake Chad, in the Sahel region of Africa (and once the sixth largest lake in the world) has shrunk by 90% over the last 40 years, primarily due to increased agricultural and municipal water withdrawals, but also due in-part to climate change and persistent droughts (Gao et al., 2011). Other prominent examples of shrinking inland lakes include Lake Urmia in Iran, the Aral Sea in Kazakhstan and Uzbekistan, and the Salton Sea in California. The economic benefits accruing to the policy decisions that have led to shrinking lakes are often evident – increased agricultural production and municipal development enabled by the diverted water – while the health costs associated with lake-sourced air pollution have remained largely hidden. In light of the extensive literature connecting air pollution to human capital outcomes (Isen et al., 2017; Hanna and Oliva, 2015), productivity (Chang et al., 2016; Zivin and Neidell, 2012), and human health (Jones and McDermott, 2018; Currie et al., 2009), we are thus concerned that shrinking inland lakes are affecting economic variables of interest beyond those typically considered when making water management decisions. However, causally interpretable evidence linking lake shrinkage to air pollution and health is lacking.2 This is troubling for at least two reasons. First, global climate change is expected to exacerbate inland lake shrinkage by shrinking available natural supplies and increasing water demands for human use (e.g., Wurtsbaugh et al., 2017; Tao et al., 2015). Second, policymakers often consider the environmental effects of lake shrinkage (e.g., wetland habitats, biodiversity, recreation), but rarely the direct health consequences caused by fugitive dust emissions. By articulating the human health effects of lake shrinkage, our estimates can thusly inform inland lake management and agricultural water policy decisions, such as, for example, ongoing discussions surrounding management of the Aral and Salton Seas (Cohen, 2014; Micklin and Aladin, 2008). To address this knowledge gap, this paper exploits a natural experiment provided by the shrinking Salton Sea, the largest inland lake in California. The Salton Sea has receded by an average of 0.33 ft./yr. (0.1 m/yr.) in surface elevation between 1998 and 2014, exposing an estimated 16,000 acres (6475 ha) of new lake bed.3 This change has been attributed to declining inflows associated with changes in water management policy (Levers et al., 2019). As the lake has shrunk, regional PM2.5 pollution and respiratory-related health incidences have generally increased, suggestive of a causal relationship, but rigorous empirical evidence is lacking.4 Since it is well-known that air pollution exposure is likely to be endogenous (Zivin and Neidell, 2013), our empirical strategy employs an instrumental variables (IV) approach. Specifically, we estimate a dose-response function for PM2.5 and regional respiratory mortality using Salton Sea lake elevation as an instrument. Then, using the IV results, we calculate the specific contribution of Salton Sea lake shrinkage on mortality vis-à-vis induced changes in PM2.5. All models include flexible controls for weather

1

PM10 is particulate matter 10 μm or less in diameter. 2 However, there is extensive anecdotal evidence in various media outlets (e.g., USA Today, The Atlantic, The New York Times) that lake shrinkage is adversely affecting health through air pollution (e.g., James, 2017; Iovenko, 2015; Barringer, 2014). 3 Calculations are based on elevation-surface area relationships from the US Bureau of Reclamation (1997) and annual Salton Sea elevation data from the USGS National Water Information System. 4 PM2.5 is particulate matter 2.5 μm or less in diameter.

and also control for other potential environmental and socioeconomic drivers of PM2.5 and mortality. Three conclusions arise. First, dose-response estimates show that each one-foot (0.30 m) drop in Salton Sea lake elevation is associated with a 0.276 μg/m3 [95% Confidence Interval: 0.108, 0.444] or 2.6% [0.71%, 4.49%] average increase in PM2.5 concentrations in the two southern California counties containing the lake (Imperial and Riverside counties). For reference, at the volumetric range of the Salton Sea in our data, each one-foot drop in elevation corresponds to an approximately 3500-acre increase in exposed lake bed area. Second, results indicate that Salton Sea-induced changes in PM2.5 over 1998–2014 led to increases in lower respiratory mortality of 0.06/yr.–0.66/yr. per 100,000 people, or, 1.4/yr.–15.6/yr. respiratory deaths in aggregate in the counties surrounding the lake. Cumulative lower respiratory mortality impacts due to Salton Sea shrinkage total 4.2 per 100,000 over 1998–2014, or 99.1 additional respiratory deaths in aggregate. Annual economic costs associated with observed mortality impacts are approximately $13.2–$147.3 million per year. Finally, the causal interpretation of our results is supported by mortality and air pollution falsification tests showing no impacts on outcomes known to be unaffected by PM2.5 and the Salton Sea, respectively. Internal validity test results show no significant changes in key socioeconomic and demographic indicators concurrent with observed lake elevation changes. This paper contributes to the literature in at least three ways. First, it provides the first dose-response function estimates of a relationship between inland lake elevation and PM2.5. These are not only the first such estimates for the Salton Sea, but to the best of our knowledge, for any shrinking lake around the world.5 Second, we provide the first monetized estimates of the specific contribution of lake-induced changes in PM2.5 on regional lower respiratory mortality. Finally, we employ high resolution 0.1°-×-0.1° gridded PM2.5 satellite data. This contrasts with most research on PM2.5 and health, where it is more common to use spatially-limited monitored air pollution data in conjunction with various interpolation techniques (e.g., Levinson, 2012; Currie et al., 2009). While such an approach may be acceptable and even preferable for investigations in large urban areas with many monitoring stations, it is more questionable in rural areas, such as around large shrinking lakes, which have few or no monitoring stations.6,7 Though the application in this paper is specific to the Salton Sea, the empirical strategy we deploy and the information obtained (specifically, the magnitude of effect estimates) about how shrinking lakes relate to air pollution and human health have far-reaching global implications on inland lake management and water use policy, in addition to deepening our understanding of the indirect impacts of climate change. Applications of this work to other shrinking inland lake systems (e.g., Lake Urmia in Iran, Lake Chad in Africa, Aral Sea in Central Asia, Lake Poopo in Bolivia, etc.) are both varied and wide, especially to the extent that the economic costs of lake shrinkage are found to be significant. 2. The Salton Sea, air quality, and human health Located in southeastern California about 170 miles southeast of Los Angeles, the Salton Sea is an inland terminal lake in a closed basin, meaning that it has no primary water outflows; water flows into the lake, but not out (Fig. 1). It formed in 1905 in what was then called the Salton Sink after an irrigation system breach inadvertently diverted the entire flow of the Colorado River away from the Sea of Cortez and 5 There are studies that have investigated PM levels around shrinking lakes (e.g., Frie et al., 2017; Gholampour et al., 2015; King et al., 2011), and at least one recent article specific to the Salton Sea that has modeled future PM impacts of lake shrinkage (Parajuli and Zender, 2018). However, regression-based dose-response estimates, like those estimated here, have not been previously published, to the best of our knowledge. 6 Sullivan and Krupnick (2018) report that in 2015 79% of US counties did not have a PM2.5 monitoring station. 7 PM2.5 satellite data is increasingly being used in environmental health research (e.g., McGuinn et al., 2016; Hyder et al., 2014).

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Fig. 1. Map of the Salton Sea and the surrounding area.

into the sink for nearly two years (DeBuys, 2001). In the century since, the lake's levels have been sustained by agricultural runoff from the farming regions of the Imperial and Coachella Valleys, with levels rising and falling in response to changing inflows as agricultural practices changed (Fleck, 2016). Today, the Salton Sea is the largest inland lake in California with a surface area of approximately 343 sq. mi. or 889 km2. While in many cases increased use of water for agricultural purposes causes lakes to shrink by reducing inflows (see for example Lake Chad, Gao et al., 2011), exposing shorelines and increasing air pollution, the opposite has happened at the Salton Sea. Policy decisions made in the late 1990s and early 2000s reduced available water for agricultural use, which in turn reduced agricultural runoff and the resulting inflows to the Salton Sea. With evaporation greater than inflows, the lake began shrinking in the late 1990s; a practice that has continued into the present. It was recognized as early as the late 1990s that reduced inflows would expose new shoreline playas with a resulting increase in air pollution and public health impacts (Cohen et al., 1999). The state of California committed to mitigating the resulting air quality problems, but the legal framework under which the decisions were made separated the water policy processes from the resulting public health impacts leaving the commitments unenforceable (Cantor, 2016; Cohen, 2014). While much of the scientific and political attention in the years that followed focused on ecosystem effects of a shrinking Salton Sea, such as loss of bird and fish habitat, increasing attention has been focused in recent years on the public health implications (Johnston et al., 2019; Barnum et al., 2017). While there has been extensive research into the relationship between shrinking lakes and air pollution, with presumed implications for public health, and more generally on the health implications of wind-blown dust, there has been little research on the direct health links in the specific case of windblown dust associated with exposed

shoreline from shrinking lakes (Johnston et al., 2019). This applies to both the etiology of respiratory and other health problems linked specifically to dust from exposed shorelines, as well as to the associated public health costs. It can be qualitatively assumed that increasing air pollution will impose public health costs, but understanding those public health costs in more detail is critical to the use of economic analysis to inform policy choices when making water management decisions. Specifically, as water managers in arid environments (such as California and other southwestern US states) consider water diversions for urban municipal and/or agricultural use, and those diversions result in reductions to lake surface areas, a potential tradeoff is created between water use and human health vis-à-vis exposed shorelines. While the environmental impacts and costs of drawing down inland lakes have been previously documented (e.g., Liu et al., 2013), the connection to air pollution and public health is largely absent both in the literature and in the public discourse surrounding water management in arid environments. However, we are not the first to investigate the air pollution consequences of the shrinking Salton Sea. In Parajuli and Zender (2018), a weather and chemical forecasting model (WRF-Chem) was used to predict future Salton Sea exposed lake bed area and associated PM10 dust emissions. Based on their modeling, Parajuli and Zender (2018) predicted that by 2030, about 40% of the year 2000 lake bed will be exposed to wind erosion, increasing PM10 emissions by an average of 11%, though with significantly larger emissions in some localized areas. However, the authors did not consider any public health impacts nor did they employ a regression-based strategy to estimate empirical relationships between historical Salton Sea lake shrinkage, air pollution, and human health, as is done here. Additional studies on the Salton Sea and air pollution include: Frie et al. (2017), King et al. (2011), and Alonso et al. (2005). As with Parajuli and Zender (2018), these works are

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primarily modeling exercises and none of them specifically measure impacts to public health. 3. Data

elevation levels. For some model specifications later in the paper, we also use data on Salton Sea lake surface area (in acres). The relationship between lake elevation and lake surface area was estimated as part of a geographic survey of the Salton Sea as reported in US Bureau of Reclamation (1997).

3.1. PM2.5 data 3.3. Mortality data The source of the PM2.5 satellite data used is van Donkelaar et al. (2016) and van Donkelaar et al. (2015). These data are chiefly obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments aboard NASA's Terra and Aqua satellites. The MODIS instruments measure aerosol optical depth (AOD) by observing how the atmosphere reflects and absorbs visible and infrared light. Particle density in the atmosphere will alter the reflection and absorption process, thus allowing for precise measurements of AOD to occur as the satellites orbit the Earth. The satellite orbits are calculated such that they pass over approximately the same spot on Earth at roughly the same time each day. Thus, each grid cell has one observation per day. Space-based measurements of AOD are correlated with surface PM2.5 concentrations, but it is important to note that the satellites are not measuring PM2.5 directly. To calculate surface PM2.5, van Donkelaar et al. (2016) and van Donkelaar et al. (2015) appropriately combine the MODIS data with other satellite data and a chemical transport model (GEOS-CHEM). Finally, the resulting estimates are calibrated using ground-based PM2.5 monitored data. The data made publicly available by van Donkelaar et al. are averaged by year at several spatial resolutions, and we make use of the 0.1°-×-0.1° (~11 km-×-11 km) gridded US data for 1998–2014, inclusive.8 Fig. 2 illustrates the gridded PM2.5 product obtained from van Donkelaar et al. (2016) for southern California for every other year between 1999 and 2013. The Salton Sea is outlined in the center of the map. PM2.5 concentrations are relatively high around the lake and generally appear to be worsening over time (especially in 2007), though there is significant temporal variation. However, whether or not the Salton Sea is responsible for the elevated emissions around the lake or in Imperial and Riverside counties is unclear from the figure. This is because the figure only illustrates overall trends in PM2.5, but does not isolate the specific Salton Sea contribution from other emissions sources. For example, it is possible that the observed air quality changes in Fig. 2 are being driven by non-Salton Sea factors (e.g., changes in automobile emissions, mineral dust emissions, wildfire smoke, etc.). Or, alternatively, it is possible that the Salton Sea contribution to regional PM2.5 levels is sufficiently small, even if it is increasing over time as the lake shrinks, relative to changes in other emissions sources. This is where a regression model will be useful because it can help us isolate the specific Salton Sea component from other background changes. One possible explanation for the elevated PM2.5 concentrations in Imperial and Riverside counties is the high mineral dust emissions known to exist in this arid region. For example, Frie et al. (2017) show that non-playa sources of PM around the Salton Sea contribute 45% of daily PM10 mass, while Salton Sea playa contribute 8.9% of daily PM10 mass. This large magnitude difference suggests that isolating the specific contribution of lake elevation changes to PM from other sources is an empirical challenge, necessitating the inclusion of many control covariates in our regression models. 3.2. Salton Sea elevation data Annual Salton Sea elevation data were obtained from the USGS National Water Information System. Since the Salton Sea lies below sea level, the elevation data are negative with units of feet below sea level. We take the absolute value of the elevation data to make them positive, but note that the interpretation now is that an increase in absolute value measured lake elevation corresponds to an actual decrease in lake 8

The data are available at: http://fizz.phys.dal.ca/~atmos/martin/?page_id=140.

County-level mortality data for 1998–2014 come from the US CDC WONDER database. Age-adjusted rates per 100,000 are used for lower respiratory mortality, which includes causes of death such as bronchitis, respiratory tract infections, COPD, asthma, and lung disease. Respiratory diseases are the most common impacts of PM2.5 exposure (Xing et al., 2016) and past anecdotal evidence suggests that the Salton Sea is associated with above-average respiratory illnesses (e.g., Iovenko, 2015; Cohen and Hyun, 2006). Additional mortality data were also collected on suicides, transportation-related accidents, and non-transportation accidents, which are used in falsification tests later in the paper. To match the mortality unit of analysis (county-level) with the PM2.5 unit of analysis (currently, 0.1°-×-0.1° grid cells), we take the annual average of all grid cells, separately by county. 3.4. Weather and other data It is important that our empirical models control for the main nonSalton Sea causes of PM2.5 emissions in order to eliminate confounding effects. To this end, we collected data on: (i) weather outcomes (precipitation, temperature, wind patterns), (ii) wildfire acres burned in California, (iii) total agricultural harvest area, (iv) vehicle miles driven in California, and (v) indicators of economic activity (unemployment rate and household income).9 Station-level daily weather data on total precipitation, maximum temperature, and minimum temperature come from the NOAA National Centers for Environmental Information (NCEI). Following a similar approach in Levinson (2012), we construct a 25-mile circle around the population-weighted centroid of each county and take a weighted average of the weather monitored data within the circle, where the weights are equal to the inverse of the square root of station's distance to the population-weighted centroid (using exact latitude and longitude coordinates). The weightings are done separately by weather outcome type and year. Annual minimum and maximum temperature were then averaged to obtain mean annual temperature. We also obtained daily data on wind speed and direction from NCEI's North American Regional Reanalysis using their 32 km-×-32 km grid cell data on the East-West and North-South wind vectors. An inverse grid cell-to-county centroid distance weighting approach was again used to construct annual wind conditions at the county-level. We include flexible weather and wind controls in all PM2.5 models, as discussed in the modeling section below. Wildfire burn area data were obtained from the CAL FIRE incident database and consist of estimates of the total number of acres burned in California per year. We control for wildfire burn area since wildfire smoke is a significant source of PM2.5 emissions.10 9 We do not control for secondary organic sources of PM2.5 nor do we control for nonSalton Sea mineral dust emissions. Combined satellite-modeled estimates show that levels of both of these components of PM2.5 are declining over our study period for the southwestern US (van Donkelaar et al., 2019; Table 3), though models have a difficult time constraining mineral dust (Philip et al., 2017). By contrast, ground-based monitored data from the IMPROVE network shows slightly increasing mineral dust concentrations in the southwest over 2002–2015 (Achakulwisut et al., 2017). If mineral dust emissions in the Salton Sea region are in fact increasing concurrent with, but causally distinct from declining lake elevation, then this might cause us to find a spurious statistical relationship between lake elevation and PM2.5. However, it is more likely that both Salton Sea and non-Salton Sea mineral dust emissions are increasing over time, thus, not controlling for the latter might upward bias our results if non-lake emissions trend closely with annual Salton Sea elevation. 10 We also ran models controlling for wildfire burn area across all US states using data from the National Interagency Fire Center. However, results were little changed by doing so.

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5

1999

2001

2003

2005

2007

2009

2011

2013

Fig. 2. PM2.5 concentrations for Southern California (0.1°-×-0.1° Gridded Satellite Estimates). Source: van Donkelaar et al. (2016) and van Donkelaar et al. (2015). Notes: PM2.5 concentration in units of micrograms per cubic meter (μg/m3). The white grid cell at the center of the Salton Sea indicates that no estimate was made at that point, which is because that grid cell contains 100% water.

Agricultural harvest data were collected from the USDA National Agricultural Statistics Service (NASS) five-year Census of Agriculture datasets; 2002, 2007, and 2012. Data consist of the total acres harvested per year in a given county. Like wildfire smoke, agricultural production activity is also a significant source of PM2.5 (Lelieveld et al., 2015) and the area around the Salton Sea is a large agricultural producing region

(e.g., Imperial Valley). Linear interpolation was used to fill-in missing data points in-between ag census years. State-level annual vehicle miles driven data were obtained from the US DOT Highway Statistics Series and the US DOT Bureau of Transportation Statistics. Lastly, data on annual county-level unemployment rates come from the US Bureau of Labor Statistics and information on annual

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median household income was obtained from the US Census Bureau Small Area Income and Poverty Estimates (SAIPE) program. Additional demographic and socioeconomic data were also collected to control for potential confounders of mortality. Specifically, we obtained data on county-level poverty rates from SAIPE, county-level educational attainment data from the American Community Survey, percent of the county that is Hispanic or African American, also from the American Community Survey, and county-level smoking prevalence data from the US CDC Behavioral Risk Factor Surveillance System (BRFSS). 3.5. Summary statistics In our baseline analysis, we focus on Imperial and Riverside counties since these are the two southern California counties that completely contain the Salton Sea (see Fig. 1). In what follows, we will refer to these two counties as the “Salton Sea counties”. For reference, the Salton Sea counties have a 2010 US Census combined population of 2.36 million and a combined land area of 11,383 miles2 (29,482 km2).11 Summary statistics for the final dataset are presented in Table 1. The unit of observation is a county-year, and since our baseline analysis includes only the two Salton Sea counties, the sample size is small. The small sample size motivates us to use small sample statistics later in the paper.12 Mean PM2.5 concentration over 1998–2014 is 9.34 μg/m3 in the Salton Sea counties and average lake elevation is 229.64 ft. below sea level. For reference, average PM2.5 in the US in 2017 was 8.02 μg/m3 per the US EPA.13 The lower respiratory mortality death rate in our sample is 39.25 per 100,000, which is very similar to the 2016 US average of 40.6 per 100,000 from the US CDC.14 4. Empirical methods Our empirical strategy is to determine the relationship between Salton Sea elevation and regional PM2.5 concentrations, and, separately, the specific impact of Salton Sea-induced changes in PM2.5 on regional lower respiratory mortality outcomes. Thus, there are two statistical models that must be estimated: (i) a dose-response function for elevation and PM2.5, and; (ii) a concentration-response function for PM2.5 and lower respiratory mortality. Since we are concerned, as is generally the case in environmental research, that exposure to PM2.5 pollution is likely endogenous, an instrumental variables (IV) approach is employed, which prior literature has shown is one way for dealing with pollution endogeneity (Zivin and Neidell, 2013). Changes in Salton Sea elevation will be used as an instrument for PM2.5, which will thus enable direct estimation of the dose-response function as a first-stage by-product of a two-stage least squares (2SLS) regression model. A similar empirical approach to the one used here can be found in Jones and McDermott (2018). Our key identifying assumption is that Salton Sea elevation, and thus changes in lake bed exposure area, provides a credible source of exogenous variation in PM2.5 (instrument relevance), but has no independent effect on lower respiratory mortality (the exclusion restriction). A strong case can be made for instrument relevance based on the many physical science studies that have carefully documented how Salton Sea exposed lake bed is a significant source of PM emissions (e.g., Parajuli and Zender, 2018; King et al., 2011; Alonso et al., 2005).15 Moreover, the 11 Later in the paper, we also consider impacts to neighboring counties/states in California and Arizona. 12 The sample size is substantially increased in some checks later in the paper where we add more counties to the analysis. The findings are similar by doing so. 13 See US EPA “Particulate Matter (PM2.5) Trends”: https://www.epa.gov/air-trends/ particulate-matter-pm25-trends. 14 See Figure 4 in US CDC “Mortality in the United States, 2016”: https://www.cdc.gov/ nchs/products/databriefs/db293.htm. 15 This literature has primarily focused on PM10 and not PM2.5, as is done here. However, we know that PM10 and PM2.5 are highly correlated and that PM2.5 is more damaging to human health since it can penetrate deeply into the lungs. Thus, we would suspect that if we used PM10 data in place of PM2.5 that we might observe a stronger relationship between Salton Sea elevation and PM levels, but a weaker relationship to human health.

Table 1 Summary statistics for Salton Sea counties (1998–2014). Sources: US CDC WONDER, US CDC BRFSS, USDA NASS, CAL FIRE, US DOT, US Census Bureau, US BLS, NOAA NCEI, NOAA NARR, and USGS. PM2.5 satellite data from van Donkelaar et al. (2016) and van Donkelaar et al. (2015). For Imperial and Riverside counties in California. Variable

Mean

Std. dev.

PM2.5 concentration (μg/m3) 9.34 0.81 Salton Sea elevation (ft. below sea level) 229.64 1.79 Lower respiratory mortality (per 100,000) 39.25 14.20 Wildfire burn area (in thousands of acres) 533.86 340.22 Agricultural harvest (in thousands of 318.29 147.66 acres) Vehicle miles driven 321,037 10,062 Unemployment rate (%) 14.73 8.02 Poverty rate (%) 18.51 5.42 Median household income ($) 42,113 9382 Proportion county high school degree (%) 70.05 8.24 Proportion county college degree (%) 15.57 3.85 Proportion county Hispanic (%) 59.46 18.48 Proportion county African American (%) 4.93 1.42 Smoking prevalence (%) 16.01 2.17 Annual wind speed (m/s) 3.51 0.49 Annual wind direction (°) 265 32.48 Annual precipitation (in) 2.15 0.46 Annual mean temperature (°F) 71.24 4.99

Observations 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34 34

state of California itself has formally recognized the causal connections between Salton Sea elevation and regional air quality as part of their 2007 Salton Sea Restoration Program Final Programmatic Environmental Impact Report (State of California PEIR, 2007). In terms of the exclusion restriction, it seems highly unlikely that Salton Sea elevation would impact lower respiratory mortality outside of the PM2.5 pathway. According to the World Health Organization (WHO), the major risk factors for respiratory diseases and mortality are, in no particular order, air pollutants (indoor and outdoor), tobacco smoke, and occupational agents (WHO, 2007). Changes in lake elevation levels are likely to only impact the air pollution risk factor, but not smoking habits or exposure to occupational toxins. However, the WHO also acknowledges that diet and nutrition are possible, but not major risk factors for respiratory illnesses (WHO, 2007), and to the degree that the size of the Salton Sea could have impacts on regional socioeconomic conditions (e.g., employment opportunities, income, demographics, etc.), it is plausible that diet and nutritional impacts could result given past evidence that links socioeconomic outcomes and nutrition (e.g., Reis, 2012).16 As a way to increase the likelihood that the exclusion restriction holds, the empirical specifications therefore incorporate controls for many observable socioeconomic and demographic characteristics (e.g., unemployment rate, poverty rate, household income, race, etc.).17 Additionally, a test of internal validity shows no impact of Salton Sea elevation on key socioeconomic and demographic indicators (see Appendix A). Thus, we believe that a strong case exists to support the exclusion restriction (at least conditionally after controlling for the relevant covariates). A 2SLS model is employed to jointly estimate the dose-response and concentration-response functions. In the first-stage regression, PM2.5 concentration is predicted using Salton Sea elevation and a set of controls for other confounders of PM2.5. The dose-response function is estimated as, PM2:5ct ¼ β0 þ β1 jElevationt j þ X 0ct β3 þ εct

ð1Þ

16 At least historically (pre-1980s), local economic conditions around the Salton Sea were influenced by the size and quality of the lake vis-à-vis the recreation and tourism industry (McClurg, 1994). Today, the evidence that continued marginal changes in lake elevation leads to further changes in socioeconomic characteristics seems more suspect since the lake is generally already eschewed by the public (i.e., any residential sorting has already occurred). 17 County fixed effects were also investigated as a robustness check, but the results were little changed with their inclusion.

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where PM2.5ct is the satellite-measured PM2.5 concentration in county c in year t, |Elevationt| is the absolute value of lake elevation in year t, Xct is a vector of controls for other confounders of PM2.5 (five temperature bins (b25 °F, 25°–45 °F, 45°–65 °F, 65°–85 °F, and N85 °F), quadratic precipitation, three wind direction bins (0–120°, 120–240°, and 240–360°), three wind speed bins (0–2 m/s, 2–4 m/s, N4 m/s), wildfire acres burned, agriculture harvest area, vehicle miles driven, and the unemployment rate), and εct is the idiosyncratic error term.18 Inclusion of the weather covariates will remove their effects on the elevationPM2.5 relationship – e.g., precipitation impacts on elevation and PM2.5. The coefficient of interest is β1, which describes the relationship between lake elevation and PM2.5 levels in the Salton Sea counties. In the second-stage, the concentration-response function is estimated by using predicted PM2.5 concentration from Eq. (1) in place of actual pollution levels, d ct þ X 0 ρ þ Z 0 ρ þ Yeart þ γ Mort ct ¼ ρ0 þ ρ1 PM2:5 ct ct 3 ct 4

ð2Þ

where Mortct is the lower respiratory mortality per 100,000 in county c d ct is predicted PM2.5 from the first-stage, Xct is the in year t, PM2:5 same set of control covariates as used in the first-stage, Zct is a set of additional variables that are included to eliminate possible confounders of mortality (all-age poverty rate, high school diploma attainment, college degree attainment, median household income, percent county Hispanic, percent county African American, and smoking prevalence), and γct is the second-stage error term.19 The coefficient of interest is ρ1, which captures the impact of PM2.5 on respiratory mortality. Given our small sample size, we follow the recommendations of Imbens and Kolesar (2016) and use their modification of the McCaffrey and Bell (2002) procedure in order to improve the robustness and consistency of our standard errors. The modification consists of two steps: (i) removing some of the bias in the Eicker-Huber-White robust standard errors, and; (ii) using a degrees-of-freedom adjustment that matches the moments of the variance estimator to one of a chisquared distribution. More detail is provided in Imbens and Kolesar (2016). In the results that follow, we report the small sample adjusted standard errors.20 5. Results 5.1. First-stage dose-response function results (lake elevation and PM2.5) We begin by showing the dynamic dose-response relationship between Salton Sea elevation and regional PM2.5 concentrations. To improve the exposition of the dose-response results, we estimate a variant of Eq. (1) where lake elevation is interacted with a full set of year indicators, and all other variables remain unchanged. The results 18 More detail on the variables in Xct is provided here. The temperature and wind direction/speed bins are count variables corresponding to the number of days in a year that average temperature/wind direction/wind speed fell into a particular bin. Each bin is estimated as a separate variable. Precipitation is a continuous quadratic variable (level and squared term) for average annual county-level precipitation. Wildfire acres burned is a continuous variable for total acres burned in California per year. Agriculture harvest area is a continuous variable for annual county-level acreage of ag harvest. Vehicle miles driven is a continuous variable for annual total vehicle miles driven in California. The unemployment rate is a continuous variable of the county-level annual unemployment rate. 19 More detail on the variables in Zct is provided here. Poverty rate is a continuous variable for the annual county-level all-age poverty rate. High school diploma and college degree attainment are each a continuous variable of the annual percentage of the county that has obtained a HS diploma or a college degree (Bachelor's or higher). Median household income is a continuous variable of annual county-level median household income. Hispanic and African American are continuous variables of the annual percentage of the county population that is Hispanic or African American. Smoking prevalence is a continuous variable of the annual percentage of the county population that reports smoking any cigarettes. 20 Results are similar if small sample adjusted county-year clustered standard errors are used instead of the small sample non-clustered standard errors.

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from this regression are presented in Fig. 3. Panel A shows the annual marginal impact of elevation on PM2.5 and panel B shows the cumulative impact since 1998, the base year. Overlaid on each graph is a line depicting Salton Sea lake elevation, which is falling for all but one data year. There is a clear and statistically significant association between lake elevation and regional PM2.5 levels. This is evidenced in two ways. First, as the Salton Sea shrinks over time, PM2.5 concentrations significantly increase relative to the 1998 baseline. For instance, from panel B, we see that falling lake elevation between 1998 and 2014 is estimated to have cumulatively raised PM2.5 concentration by a total of 1.55 μg/ m3 [95% CI: 0.91, 2.07], however with a moderate level of uncertainty. The large confidence intervals are likely driven by noise in the data due to the fact that we are using county-level averages, which will capture not only PM2.5 from the Salton Sea, but also PM2.5 from other sources. Interestingly, in the one year when the lake size actually increases (in 2005), there is a corresponding negative marginal change in PM2.5 (see the negative data point for 2005 in panel A). That is, as the area of the exposed lake bed declines, PM2.5 pollution falls slightly. Second, the rate of change in marginal PM2.5 impacts generally mirrors the rate of change in Salton Sea elevation. For example, we can see in panel A that in the years where elevation only falls slightly (e.g., 1999–2001) the corresponding regression estimated dose-response impacts on PM2.5 are also small (the marginal changes are near zero). Conversely, when the decrease in elevation is larger in magnitude (e.g., 2009 & 2014), the estimated marginal PM2.5 impacts are also correspondingly larger. This suggests a causal connection since greater annual contractions of the lake would be expected to expose larger areas of lake bed, thereby increasing (at least the potential for) emissions of PM2.5. 5.2. Full IV regression results Results from estimating versions of the full 2SLS model in Eqs. (1) and (2) are presented in Table 2, columns (2)–(4).21 Each column corresponds to a slightly different specification of the first-stage model. Column (2) corresponds to the model explicitly presented in Eq. (1); this is our baseline specification. Column (3) uses log(PM2.5) as the dependent variable rather than its level, and column (4) uses Salton Sea surface area as an alternative instrument in place of elevation. For reference purposes, and to demonstrate the need for an instrumental variables design, we have also presented the simple OLS results of the second-stage equation in column (1), which show no significant association between PM2.5 and respiratory mortality (in fact the coefficient on PM2.5 is negative). Endogeneity is likely biasing the OLS results, as is commonly the case in misidentified models of pollution and health (Zivin and Neidell, 2013). The IV results in columns (2)–(4) provide strong evidence that lake elevation and lake surface area are associated with regional PM2.5 concentrations. The baseline specification results suggest that each one-foot (0.30 m) drop in Salton Sea elevation corresponds to a 0.276 μg/m3 [0.108, 0.444] increase in regional PM2.5.22 Alternatively, from the log specification in column (3), each one-foot drop in elevation increases PM2.5 by 2.6% [0.71%, 4.49%]. While this figure may seem high, it is worth nothing that at this volumetric range of the Salton Sea, each one-foot drop in elevation exposes, on average, an additional 3500 acres of new lake bed. As a robustness check, using lake surface area as the instrument in place of elevation leads to similar findings (column 4): each 1000-acre reduction in Salton Sea surface area increases PM2.5 by 0.09 μg/m3 [0.07, 0.11]. Thus, a 3500-acre reduction in surface area, which is approximately equivalent to a one-foot drop in lake elevation, would cause PM2.5 to rise by 0.32 μg/m3 [0.25, 0.39], which is very close to the findings when elevation is used as an instrument instead. Results 21 For brevity, only the key lake elevation and PM2.5 results are shown in Table 2 and the full results are provided in Appendix B. 22 Recall that since we have taken the absolute value of elevation, an increase in elevation now corresponds to an actual shrinking of the lake.

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B.A. Jones, J. Fleck / Science of the Total Environment 712 (2020) 136490

99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14

19

19

98

234

228 232 230 |Elevation| (feet below sea level)

226

PM2.5 Concentration (micrograms/cubic meter) -0.1 0 0.1 0.2 0.3 0.4

Panel A: Annual Marginal PM2.5 Impacts

Annual Change in PM2.5 Salton Sea Elevation

19 98 19 99 20 00 20 01 20 02 20 03 20 04 20 05 20 06 20 07 20 08 20 09 20 10 20 11 20 12 20 13 20 14

234

228 232 230 |Elevation| (feet below sea level)

226

PM2.5 Concentration (micrograms/cubic meter) 1.2 1.6 2 0 0.4 0.8

Panel B: Cumulative PM2.5 Impacts

Cumulative Change in PM2.5 Salton Sea Elevation

Fig. 3. Impacts of Salton Sea elevation on PM2.5 (dose-response function regression results). Notes: year 1998 is set as the baseline. Panel A shows the annual incremental effects of elevation changes on PM2.5. Panel B shows the cumulative change in PM2.5 due to elevation changes since 1998. Error bars correspond to 95% confidence intervals. Results are from a regression of PM2.5 concentration (in levels) on a full set of elevation × year interactions and include controls for wildfire acres burned, vehicle miles driven, agricultural harvest acres, unemployment rate, five temperature bins, quadratic precipitation, three wind speed bins, and three wind direction bins. Cumulative effects calculated by appropriately summing the incremental effects.

are thus robust to the use of alternative instruments for the size of the lake. Observe that all the first-stage F-statistics are between 10 and 20, providing evidence in support of instrument relevance. From the second-stage results, we find that each microgram per cubic meter increase in PM2.5 is associated with 2.74 per 100,000 [0.695, 4.79 per 100,000] additional instances of lower respiratory mortality. The large range on this point estimate may have to do with noise in the instrument and the fact that county-level data are used, which will increase uncertainty. A similar concentration-response relationship is observed when surface area is used as the instrument instead (2.73

per 100,000). In the log model, each 1% increase in PM2.5 results in an additional 0.19 per 100,000 [0.03, 0.35 per 100,000] respiratory deaths. Overall, IV results provide evidence that changes in Salton Sea elevation impact regional PM2.5 concentrations, and, that PM2.5 is positively associated with respiratory mortality. 5.3. Salton Sea-induced PM2.5 impacts on respiratory mortality To calculate the annual effects of Salton Sea-induced changes in PM2.5 on respiratory mortality (as opposed to the general impact of

B.A. Jones, J. Fleck / Science of the Total Environment 712 (2020) 136490 Table 2 Impacts of PM2.5 on lower respiratory mortality in Salton Sea counties (IV regression results).

First-stage results (PM2.5) Elevation (absolute value)

(1)

(2)

(3)

(4)

OLS

IV (levels)

IV (log PM2.5)

IV (surface area instrument)

–

0.276⁎⁎⁎ (0.080) –

0.026⁎⁎⁎ – (0.009) – −0.00009⁎⁎⁎ (0.00001) 233.03⁎⁎⁎ 35.35⁎⁎⁎ −185.94⁎⁎⁎ (69.59) (9.67) (53.29)

Lake surface area Constant

Environmental and economic controls Weather controls First-stage F-statistic

–

–

Yes

Yes

Yes

Yes 11.91

Yes 13.57

Yes 11.98

Second-stage results (lower respiratory mortality per 100,000) PM2.5 concentration −1.56 2.74⁎⁎ 18.74⁎⁎ (2.92) (0.995) (7.83) Constant 2616.85⁎⁎⁎ 36.47⁎ 21.07 (903.89) (31.89) (34.67)

2.73⁎⁎ (0.974) 36.19 (31.61)

Instruments from first-stage Sociodemographic controls Adj. R-squared Sample size

Yes Yes 0.956 34

No Yes 0.985 34

Yes Yes 0.946 34

Yes Yes 0.945 34

PM2.5 on mortality), we combine the elevation × year interacted firststage regression estimates from Fig. 3 with the second-stage baseline IV regression estimates from Table 2. Specifically, we multiply the first-stage estimate that describes how a given year's elevation change impacts PM2.5 by the second-stage estimate that describes how PM2.5 changes impact lower respiratory mortality. So, for example, for the year 2000, we found in Fig. 3 that the marginal impact of elevation on PM2.5 in that year was 0.057 μg/m3. Multiplying this by the second-stage concentration-response estimate from the baseline specification in Table 2, 2.74 per 100,000 lower respiratory deaths per 1 μg/ m3 increase in PM2.5, produces an estimate of 0.16 per 100,000 lower respiratory deaths. Hence, in the year 2000, the empirically estimated change in PM2.5 caused by the year-over-year elevation change in the Salton Sea resulted in an additional 0.16 per 100,000 lower respiratory deaths in the Salton Sea counties. These calculations were repeated for each year and the final results are presented in Fig. 4. Fig. 4 shows both the annual marginal impacts and the cumulative impacts (since 1998) of induced changes in PM2.5 on respiratory mortality. Between 1998 and 2014, changes in Salton Sea elevation resulted in a cumulative total of 4.2 per 100,000 additional lower respiratory deaths in southern California. However, mortality impacts are subject to variability as evidenced by the over time fluctuations in the annual marginal impact curve. Ignoring 2005 (when lake elevation slightly rose), impacts are as low as 0.06/yr. per 100,000 in 2001 and as high as 0.66/yr. per 100,000 in 2014. On average, respiratory mortality increases by 0.24 per 100,000 per year over the 1998–2014 period. The average annual elevation change over this time period was 0.35 ft./yr. (0.11 m/yr.). Thus, on average, each 0.35 ft. drop in Salton Sea elevation results in an increase in regional lower respiratory mortality of 0.24 per 100,000, or, if we make a linear extrapolation, each one-foot drop in lake elevation is associated with 0.68 per 100,000 additional respiratory deaths. While these numbers may seem small, it is important to remember that they are in per 100,000 terms. The two Salton Sea counties have a combined 2010 US Census population of 2.36 million people. Thus, in aggregate, each one-foot drop in lake elevation is associated with a total of 16.0 additional respiratory deaths, or, 99.1 cumulative lower respiratory deaths over the entire 1998–2014 period; approximately 5.7

Lower Respiratory Mortality (per 100,000) 4 1 2 0 3

Notes: column (1) presents OLS regression results of the second-stage when elevation is not used as an instrument. 2SLS IV regression results presented in columns (2)–(4). Column (2) uses levels of all variables. Column (3) uses log(PM2.5) in the first and second stages. Column (4) uses Salton Sea surface area (in acres) as an alternative instrument. Environmental and economic controls include: wildfire acres burned, agricultural acres harvested, vehicle miles driven, and unemployment rate. Weather controls include: five temperature bins, quadratic precipitation, three wind speed bins, and three wind direction bins. Sociodemographic controls include: poverty rate, high school diploma attainment, college degree attainment, median household income, proportion Hispanic, proportion African American, and smoking prevalence. Modified Imbens and Kolesar (2016) small sample correction used in IV regressions. ⁎⁎⁎ p b 0.01. ⁎⁎ p b 0.05. ⁎ p b 0.10.

9

98 999 000 001 002 003 004 005 006 007 008 009 010 011 012 013 014 2 2 2 2 2 2 2 2 2 2 2 1 2 2 2 2

19

Cumulative PM2.5-Induced Mortality Impacts Annual PM2.5-Induced Mortality Impacts

Fig. 4. Lower respiratory mortality impacts of Salton Sea-induced PM2.5 changes (calculated from IV regression results). Notes: year 1998 is set as the baseline. Cumulative (since 1998) and annual marginal impacts of changes in lower respiratory mortality (per 100,000 people) caused by PM2.5 changes associated with Salton Sea elevation changes are shown. Results obtained by combining first- and second-stage 2SLS regression estimates with annual Salton Sea elevation changes from year-to-year. Specifically, the first-stage elevation × year estimates from Fig. 3 were used to calculate the PM2.5 impact for a given year's elevation change. This was then multiplied by the second-stage estimate of the instrumented effect of PM2.5 on lower respiratory mortality from Table 2. Cumulative impacts calculated by appropriately summing all previous year impacts. Error bars correspond to 95% confidence intervals.

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B.A. Jones, J. Fleck / Science of the Total Environment 712 (2020) 136490

additional deaths per year. Using the range of annual deaths previously reported above (0.06/yr.–0.66/yr. per 100,000), the aggregate range of annual lower respiratory mortality outcomes caused by PM2.5 from newly exposed lake bed is 1.4/yr.–15.6/yr. in the Salton Sea counties.

Table 3 Alternative specifications (IV regressions).

6. Additional specifications and falsification tests 6.1. Alternative 2SLS model specifications To add further depth to our analysis, several alternative 2SLS model specifications are investigated. The results of these specifications are listed by column in Table 3.23 Beginning with column (1), we add San Diego County, California to the data since it straddles the northwestern edge of the Salton Sea and thus might potentially be affected by changes in exposed lake bed. Re-estimating the 2SLS model in Eqs. (1) and (2) after this change results in a slight attenuation of the elevation and PM2.5 coefficient estimates, but the main story remains. This suggests that Salton Sea elevation changes are having only small effects on San Diego County PM2.5 (i.e., the signal is weak, causing coefficient attenuation). In column (2), we add the two western-most counties in Arizona to the dataset (La Paz and Yuma counties) since the Salton Sea may potentially adversely affect air quality in these counties too. Note that our sample size has doubled now. The 2SLS results are modestly attenuated here, but the sign and significance remain. This again suggests that Salton Sea air pollution impacts are small in these two Arizona counties relative to the counties immediately surrounding the lake (i.e., Imperial and Riverside counties). But, it is comforting to see that the empirical elevation-PM2.5 relationship continues to hold after doubling our sample size. Column (3) investigates whether there is a non-linear relationship between lake elevation and regional PM2.5 concentrations. Both elevation and elevation-squared are now used as instruments. However, we observe no evidence to support a non-linear instrument story (the coefficients are insignificant). Finally, in column (4), the potential for nonlinearity in the second-stage concentration-response function is investigated. Both PM2.5 and PM2.5-squared are now included in the secondstage regression.24 Indeed, we find limited statistical evidence of a nonlinear relationship between PM2.5 and lower respiratory mortality. The positive (negative) coefficient on PM2.5 (PM2.5-squared) implies that PM2.5 impacts are increasing at a decreasing rate (i.e., a concave concentration-response function). Since concave concentrationresponse functions are often found in the PM2.5 and health literature (see discussion in Goodkind et al., 2014), this finding thus provides limited confirmatory evidence that our model specification is yielding results consistent with the extant literature. 6.2. Falsification tests To improve the causal interpretation of our results, falsification tests are performed. Falsification tests are an important component of modern causal inference techniques because, in the words of Athey and Imbens (2017), they “shed light on the credibility of the primary analysis” by showing no effects on outcomes that are known to unaffected by a given treatment or, in this context, given environmental change. For the Salton Sea, we show here: (i) that there are no impacts of changing lake elevation on pollution outcomes known to be unaffected by shrinking lakes (an “air pollution falsification test”), and (ii) that there are no impacts of PM2.5 on mortality outcomes previously known to be unaffected by particulate matter pollution (a “mortality falsification test”). To conduct the air pollution falsification test, we collected annual station-level data on Clean Air Act criteria air pollutants: carbon monoxide (CO), sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) 23 As before, for brevity, only the key lake elevation and PM2.5 results are shown in Table 3 and the full results are provided in Appendix B. 24 We omit the first-stage PM2.5-squared regression results from the table.

First-stage results (PM2.5) Elevation (absolute value) Elevation squared (absolute value) Constant

Environmental and economic controls Weather controls State FE First-stage F-statistic

(1)

(2)

(3)

(4)

Adding San Diego County

Adding Arizona Border Counties

Non-linear instrument

Non-linear PM2.5

0.223⁎⁎ (0.105) –

0.196⁎⁎⁎ (0.051) –

0.276⁎⁎⁎ (0.080) –

199.64⁎⁎ (90.16)

218.59⁎⁎⁎ (55.30)

3.40 (5.84) −0.04 (0.06) −1891.22 (1901.96)

Yes

Yes

Yes

Yes

Yes No 24.69

Yes Yes 11.58

Yes No 5.67

Yes No 11.22

Second-stage results (Lower respiratory mortality per 100,000) PM2.5 concentration 1.95⁎⁎ 2.60⁎⁎ 2.32⁎⁎ (0.903) (1.22) (1.13) PM2.5 concentration – – – squared Constant 78.99⁎⁎⁎ 13.62 40.00 (28.72) (44.46) (31.95) Instruments from first-stage Sociodemographic controls State FE Adj. R-squared Sample size

Yes Yes No 0.876 51

Yes Yes Yes 0.614 68

Yes Yes No 0.946 34

233.03⁎⁎⁎ (69.59)

2.57⁎⁎ (1.21) −0.014⁎ (0.009) 96.51 (59.30) Yes Yes No 0.944 34

Notes: Column (1) adds San Diego county data to the baseline IV model. Column (2) adds Arizona counties La Paz and Yuma to the baseline model. Column (3) adds elevation squared as an instrument. Column (4) adds PM2.5 concentration squared in the secondstage. Note that the first-stage PM2.5 concentration squared equation results have been omitted. See footnote below Table 2 for descriptions of the controls. ⁎⁎⁎ p b 0.01. ⁎⁎ p b 0.05. ⁎ p b 0.10.

for 1998–2014. Data were obtained from the US EPA AirData network of monitoring stations for Imperial and Riverside counties in California. To arrive at county-level estimates, we again used the county population centroid and inverse distance weighted average approach. Finally, we regressed each criteria pollutant on Salton Sea elevation and the same set of first-stage covariates previously used. Results from these regressions are presented in Appendix C (Table C1), and they show that changes in lake elevation have no impact on CO, NO2, or O3. This is as expected, and is consistent with a causal story, since the sources of these pollutants (e.g., combustion of carbon compounds, vehicle emissions, power generation, etc.) are physically and chemically unrelated to the Salton Sea and would therefore be unaffected by changes in the exposed lake bed. Interestingly, we do see a marginally significant effect of elevation on concentrations of SO2. While an initially unexpected result, after conducting some further research, there seems to be a plausible explanation for this finding: lake emissions of hydrogen sulfide (H2S), a precursor to SO2. The Salton Sea actually emits significant amounts of H2S due to high levels of fertilizer in the lake combined with low oxygen levels, which facilitates the growth of algae-consuming bacteria that themselves release H2S (Reese et al., 2008). Elevated H2S emissions at the Salton Sea have actually been well-documented (e.g., SCAQMD, 2013; Reese et al., 2008), which is the most likely explanation for the significant SO2 coefficient in the falsification test. However, more research is required on this connection, which is outside of the scope of this paper. Nevertheless, given a plausible explanation for the SO2 result, this would not seem to invalidate the air pollution falsification test. If anything, the causal story is actually slightly strengthened by this finding because we observe SO2 impacts around a lake that is a

B.A. Jones, J. Fleck / Science of the Total Environment 712 (2020) 136490

significant contributor to SO2 vis-à-vis hydrogen sulfide, providing yet more evidence that the Salton Sea can indeed impact regional air quality along previously established causal pathways. For the mortality falsification test, we show no impact of PM2.5 on mortality outcomes known to be previously unaffected by this air pollutant. From the extant literature, PM2.5 is known to primarily affect respiratory and cardiovascular outcomes (Kampa and Castanas, 2008). Therefore, we evaluate deaths by suicide, non-transportation accidents, and transportation accidents. Our prior is that PM2.5 should have no effect on these outcomes. The 2SLS model was re-estimated using these mortality outcomes in the second-stage (in place of lower respiratory mortality) and the results are presented in Appendix C (Table C2). The same set of control covariates are included here as in the earlier baseline specification. As expected, PM2.5 has no significant impact on any of these outcomes, consistent with a causal story.

11

the extent that PM2.5 is influenced by agricultural activity, this would suggest that upstream irrigation efficiency gains, while leading to reduced Salton Sea inflows, have not simultaneously caused increased agricultural intensity or harvesting levels. 7. Conclusions

There are two additional sets of potential confounding effects that could be driving our findings. First, it is possible that other non-Salton Sea emission sources of PM2.5 are increasing at the same time as the lake shrinks in size. It has been previously shown that agricultural activity, farmland burning, and motor vehicle exhaust from the nearby USMexico border crossing at Calexico, CA also have significant impacts on regional PM concentrations in the Imperial Valley/Salton Sea lake areas (Mendoza et al., 2010; Watson and Chow, 2001). Changes in these emission sources may be falsely giving the appearance of a Salton Sea-PM2.5 effect when in fact no such effect exists, or, alternatively, it is possible that these other emissions changes are leading us to over/underestimate the true Salton Sea-PM2.5 effect. Second, there could be some other “third factor” causing the Salton Sea to shrink and simultaneously increasing regional PM2.5 emissions. Recall that the primary cause of declining Salton Sea lake elevation is reduced inflows due to upstream efficiency gains in agricultural irrigation systems.25 If the irrigation system efficiency gains have caused regional agricultural activity to increase and/or become more intensive, at the same time that they have reduced lake inflows, then this too could falsely give the appearance of a Salton Sea-PM2.5 connection (since ag activity is likely to be positively correlated with PM2.5). While our original models did control for agricultural harvest area, they did not control for agricultural intensity. To account for both sets of concerns as potential drivers of our PM2.5 results, several additional empirical checks are performed. We collected additional annual data on agricultural intensity (fertilizer use, pesticide use, and on-farm tractor/truck use from USDA NASS), farmland burning acreage (from the Imperial County Air Pollution Control District), the number of vehicular US-Mexico border crossings at Calexico (from US DOT), and the number of acres fallowed each year in Imperial Valley (from the Imperial Irrigation District) and included these variables as additional controls in our full IV models. Results from estimating models including these controls are presented in Appendix D and are summarized here. Effects of lake elevation on PM2.5 concentrations are similar here (in both magnitude and significance) to the baseline IV results shown in Table 2. While the amount of fertilizer applied, the acres of farmland burned, and the number of vehicles crossing the nearby USMexico border are, in a subset of models, at least marginally significant predictors of PM2.5, the isolated effect of Salton Sea elevation on PM2.5 is fairly similar across models. This suggests that other changes in emission sources are not leading to a false finding of a Salton Sea-PM2.5 relationship. Additionally, we show in Appendix E that both total agricultural harvest area and agricultural intensity, in terms of annual applications of fertilizers and pesticides, are largely falling over the study period. To

This paper investigated the environmental health externalities of changes in the area of exposed lake bed at the Salton Sea in southern California. Using an instrumental variables design, results suggest that each one-foot drop in Salton Sea elevation is associated with a 0.276 μg/m3 increase in regional PM2.5 concentrations in Imperial and Riverside counties. Between 1998 and 2014, the shrinking Salton Sea was associated with PM2.5 changes that increased lower respiratory mortality outcomes by 0.06/yr.–0.66/yr. per 100,000 people, or 1.4/ yr.–15.6/yr. in aggregate. Mortality outcome estimates can be monetized using the US EPA value of a statistical life (VSL) of $9.44 million (2018$) (US EPA, 2018). Performing the necessary calculations using the previously reported aggregate mortality outcome range, leads us to conclude that changes in the amount of exposed lake bed around the Salton Sea are responsible for $13.2–$147.3 million per year in lower respiratory mortality costs associated with PM2.5 exposure. The specific cost impacts differ from year-to-year depending on annual idiosyncratic elevation changes. Therefore, to present the results in a more standardized fashion, each one-foot drop in lake elevation creates, on average, $151.5 million in respiratory mortality costs in the Salton Sea counties on the basis of 0.68 per 100,000 additional respiratory deaths per foot of elevation drop.26 This is a sizeable economic damage estimate, which underscores the significant contribution that shrinking inland lakes can have on social welfare. How do our results compare to the extant literature? In a recent model of Salton Sea PM emissions, Parajuli and Zender (2018) found that a simulated increase in the area of exposed lake bed by 92,788 acres (375.5 km2) would increase localized PM10 concentrations (not PM2.5) by a maximum of 250 μg/m3 (930.7%) with an extreme lowerbound average of 6 μg/m3 (11.7%). Extrapolating from the Parajuli and Zender results, if each one-foot drop in Salton Sea elevation exposes approximately 3500 acres of lake bed, then localized PM10 concentrations would be expected to increase between 0.23 and 9.43 μg/m3 per foot drop in lake elevation. To convert PM10 into PM2.5, we follow the methodology used by the South Coast Air Quality Management District of California (SCAQMD, 2006) in assuming that PM10 is composed of between 21 and 99% of PM2.5 material, with an average of 60%. Thus, converting the extrapolated Parajuli and Zender (2018) findings, using the average PM10 to PM2.5 relationship, each simulated one-foot drop in Salton Sea lake elevation increases PM2.5 levels by 0.14–5.66 μg/m3. Though these are only simulated model estimates, our regressionbased finding of 0.276 μg/m3 per foot drop does fall within their predicted range (though at the lower end). Thus, there is some agreement between our empirical estimates and those previously modeled in the extant literature. One limitation of this work is our reliance on county-level health data. This will mask within-county heterogeneity in health, which may be significant for Riverside County, in particular. This is because the more populous western part of the county is separated from the eastern part by a large mountain range (the San Jacinto Mountains) and because the western part of the county lies in close proximity to Los Angeles (a significant source of PM2.5 emissions). This means that fugitive dust emissions from the Salton Sea will likely have little or no impact on western Riverside County PM2.5 levels, and hence, human health outcomes. Using county-level health data may therefore lead us to underestimate the health effects of Salton Sea elevation changes since part of Riverside County will only be negligibly affected by fugitive

25 To a lesser degree, changing weather patterns are also affecting the volume of the lake, but our models already include flexible controls for temperature, precipitation, and wind.

26 At current rates, the Salton Sea is shrinking by one-foot roughly every three years (or ~0.33 ft./yr.), though there is considerable heterogeneity.

6.3. Other potential PM2.5 confounding effects

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dust from the lake. Thus, the PM2.5 and health estimates found in this work are likely conservative lower-bounds on actual effects. However, using county-level data also likely increases the uncertainty of our results due to noise. Future work might consider using more granular health data to overcome this limitation. There is one bit of good news that can be gleaned from this research, which is that it appears possible that policymakers might be able to actually reverse increases in PM2.5 by reducing the area of exposed lake bed, such as through policies targeted at increasing lake water volume. We previously showed that in the one year where lake elevation actually rose in our data (2005), regional PM2.5 concentrations correspondingly fell in that year. If this relationship holds more generally, and it is unclear if it does, then proposals currently being considered by the State of California to increase water inflows into the Salton Sea, thereby eventually increasing its surface area (California Department of Water Resources, 2017), have the potential to improve human health outcomes through fewer PM2.5 emissions. As California prepares for reductions in water supply available to the farms that surround the Salton Sea (Sullivan et al., 2019), these findings offer a useful policy tool to consider how to balance benefits of water use reduction against negative public health externalities. It is a tradeoff that to date has been given little attention by policymakers, who have generally focused solely on water use and agricultural productivity. But, increasingly, local politicians have begun to insist that air quality in the communities around the Salton Sea be given significant consideration as water policy decisions are made. Salton Sea water policy decisions are “not just about water quantity, but air pollution,” one government official said during a February 2019 hearing (emphasis in original, Hanks, 2019). To the extent that future work finds similar air pollution effects caused by the shrinkage of other lakes around the world, then adding air pollution and health considerations into lake management decisions will become increasingly important. In closing, inland lakes can have multidimensional impacts on society, from serving as sources of municipal water supplies, to being wildlife and bird habitats, to impacting human health vis-à-vis particulate matter emissions, as demonstrated in this work. As further strain is placed on inland freshwater and saline lakes around the globe due to climate change, population growth, and increased agriculture demand, estimates of the environmental and economic impacts of these systems will become increasingly important. With similar challenges across the globe – at Lake Chad, the Aral Sea, Owens Lake, Lake Urmia, and elsewhere – academics and policymakers need to consider the full range of costs and benefits of their actions, not only those that fall within the narrow policy domain of water management in which most decisions affecting those inland lakes are made. The tools described above, enabling measurement of health effects, provide an additional tool in understanding and addressing those costs and benefits. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix. Supplementary results Supplementary results for this article can be found online at https:// doi.org/10.1016/j.scitotenv.2019.136490. References Achakulwisut, P., Shen, L., Mickley, L.J., 2017. What controls springtime fine dust variability in the western United States? Investigating the 2002–2015 increase in fine dust in the US southwest. J. Geophys. Res. Atmos. 122 (22), 12–449. Alonso, R., Bytnerowicz, A., Boarman, W.I., 2005. Atmospheric dry deposition in the vicinity of the Salton Sea, California—I: air pollution and deposition in a desert environment. Atmos. Environ. 39 (26), 4671–4679.

Anderson, J.O., Thundiyil, J.G., Stolbach, A., 2012. Clearing the air: a review of the effects of particulate matter air pollution on human health. J. Med. Toxicol. 8 (2), 166–175. Athey, S., Imbens, G.W., 2017. The state of applied econometrics: causality and policy evaluation. J. Econ. Perspect. 31 (2), 3–32. Barnum, D.A., Bradley, T., Cohen, M., Wilcox, B., Yanega, G., 2017. State of the Salton Sea—A Science and Monitoring Meeting of Scientists for the Salton Sea (No. 2017-1005). US Geological Survey. Barringer, F., 2014. Preserving an Accident, the Salton Sea in California, for the Good of Nature. The New York Times Accessed on 11/29/18. URL. https://www.nytimes.com/ 2014/11/11/us/-salton-sea-migrating-birds-preserving-a-mistake-made-by-ourmeddling-with-nature-.html. California Department of Water Resources, 2017. Salton Sea Management Program, Phase I: 10-Year Plan. Sacramento, CA. URL. http://resources.ca.gov/docs/salton_sea/ssmp10-year-plan/SSMP-Phase-I-10-YR-Plan-with-appendices.pdf. Cantor, A., 2016. The public trust doctrine and critical legal geographies of water in California. Geoforum 72, 49–57. Chang, T., Graff Zivin, J., Gross, T., Neidell, M., 2016. Particulate pollution and the productivity of pear packers. Am. Econ. J. Econ. Pol. 8 (3), 141–169. Chay, K.Y., Greenstone, M., 2003. The impact of air pollution on infant mortality: evidence from geographic variation in pollution shocks induced by a recession. Q. J. Econ. 118 (3), 1121–1167. Cohen, M.J., 2014. Hazard’s Toll: The Costs of Inaction at the Salton Sea. Pacific Institute. Cohen, M.J., Hyun, K.H., 2006. Hazard: The Future of the Salton Sea with No Restoration Project. Pacific Institute, Oakland, CA. Cohen, M.J., Morrison, J.I., Glenn, E.P., 1999. Haven or Hazard: The Ecology and Future of the Salton Sea. Pacific Institute for Studies in Development, Environment and Security. Currie, J., Neidell, M., Schmieder, J.F., 2009. Air pollution and infant health: lessons from New Jersey. J. Health Econ. 28 (3), 688–703. DeBuys, W., 2001. Salt Dreams: Land and Water in Low-Down California. UNM Press, Albuquerque, NM. Engelstaedter, S., Tegen, I., Washington, R., 2006. North African dust emissions and transport. Earth Sci. Rev. 79 (1–2), 73–100. Fleck, J., 2016. Water Is for Fighting Over: And Other Myths about Water in the West. Island Press. Frie, A.L., Dingle, J.H., Ying, S.C., Bahreini, R., 2017. The effect of a receding saline Lake (the Salton Sea) on airborne particulate matter composition. Environ. Sci. Technol. 51 (15), 8283–8292. Gao, H., Bohn, T.J., Podest, E., McDonald, K.C., Lettenmaier, D.P., 2011. On the causes of the shrinking of Lake Chad. Environ. Res. Lett. 6 (3), 034021. Gholampour, A., Nabizadeh, R., Hassanvand, M.S., Taghipour, H., Nazmara, S., Mahvi, A.H., 2015. Characterization of saline dust emission resulted from Urmia Lake drying. J. Environ. Health Sci. Eng. 13 (1), 82. Gillette, D., Ono, D., Richmond, K., 2004. A combined modeling and measurement technique for estimating windblown dust emissions at Owens (dry) Lake, California. J. Geophys. Res. Earth Surf. 109 (F1). Goodkind, A.L., Coggins, J.S., Marshall, J.D., 2014. A spatial model of air pollution: the impact of the concentration-response function. J. Assoc. Environ. Resour. Econ. 1 (4), 451–479. Goudie, A.S., 2014. Desert dust and human health disorders. Environ. Int. 63, 101–113. Hanks, J., 2019. Board member comments. Minutes of Imperial Irrigation District Board of Directors Special Meeting, 5 February 2019. Hanna, R., Oliva, P., 2015. The effect of pollution on labor supply: evidence from a natural experiment in Mexico City. J. Public Econ. 122, 68–79. Hyder, A., Lee, H.J., Ebisu, K., Koutrakis, P., Belanger, K., Bell, M.L., 2014. PM2.5 exposure and birth outcomes: use of satellite-and monitor-based data. Epidemiology 25 (1), 58–67. Imbens, G.W., Kolesar, M., 2016. Robust standard errors in small samples: some practical advice. Rev. Econ. Stat. 98 (4), 701–712. Iovenko, C., 2015. Toxic Dust From a Dying California Lake. The Atlantic Accessed on 11/ 29/18. URL. https://www.theatlantic.com/science/archive/2015/11/the-airbornetoxic-lake-event/414888/. Isen, A., Rossin-Slater, M., Walker, W.R., 2017. Every breath you take—every dollar you’ll make: the long-term consequences of the clean air act of 1970. J. Polit. Econ. 125 (3), 848–902. James, I., 2017. Toxic Dust and Asthma Plague Salton Sea Communities. USA Today Accessed on 11/29/18. URL. https://www.usatoday.com/pages/interactives/saltonsea/toxic-dust-and-asthma-plague-salton-sea-communities/. Johnston, J., Razafy, M., Lugo, H., Olmedo, L., Farzan, S.F., 2019. The disappearing Salton Sea: a critical reflection on the emerging environmental threat of disappearing saline lakes and potential impacts on children’s health. Sci. Total Environ. 663, 804–817. Jones, B.A., McDermott, S.M., 2018. Health impacts of invasive species through an altered natural environment: assessing air pollution sinks as a causal pathway. Environ. Resour. Econ. 71 (1), 23–43. Kampa, M., Castanas, E., 2008. Human health effects of air pollution. Environ. Pollut. 151 (2), 362–367. King, J., Etyemezian, V., Sweeney, M., Buck, B.J., Nikolich, G., 2011. Dust emission variability at the Salton Sea, California, USA. Aeolian Res. 3 (1), 67–79. Lelieveld, J., Evans, J.S., Fnais, M., Giannadaki, D., Pozzer, A., 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature 525 (7569), 367. Levers, L.R., Skaggs, T.H., Schwabe, K.A., 2019. Buying water for the environment: a hydroeconomic analysis of Salton Sea inflows. Agric. Water Manag. 213, 554–567. Levinson, A., 2012. Valuing public goods using happiness data: the case of air quality. J. Public Econ. 96 (9–10), 869–880.

B.A. Jones, J. Fleck / Science of the Total Environment 712 (2020) 136490 Liu, H., Yin, Y., Piao, S., Zhao, F., Engels, M., Ciais, P., 2013. Disappearing lakes in semiarid northern China: drivers and environmental impact. Environ. Sci. Technol. 47 (21), 12107–12114. McCaffrey, D.F., Bell, R.M., 2002. Bias reduction in standard errors for linear and generalized linear models with multi-stage samples. Proceedings of Statistics Canada Symposium, pp. 1–10. McClurg, S., 1994. The Salton Sea: the environmental and economic values of this vast inland Lake prompt local officials to launch a new restoration effort. Western Water (March/April, 3-11). McGuinn, L.A., Ward-Caviness, C.K., Neas, L.M., Schneider, A., Diaz-Sanchez, D., Cascio, W.E., ... Chudnovsky, A., 2016. Association between satellite-based estimates of long-term PM2. 5 exposure and coronary artery disease. Environmental Research 145, 9–17. Mendoza, A., Pardo, E.I., Gutierrez, A.A., 2010. Chemical characterization and preliminary source contribution of fine particulate matter in the Mexicali/Imperial Valley border area. J. Air Waste Manage. Assoc. 60 (3), 258–270. Micklin, P., Aladin, N.V., 2008. Reclaiming the Aral Sea. Sci. Am. 298 (4), 64–71. Parajuli, S.P., Zender, C.S., 2018. Projected changes in dust emissions and regional air quality due to the shrinking Salton Sea. Aeolian Res. 33, 82–92. Philip, S., Martin, R.V., Snider, G., Weagle, C.L., van Donkelaar, A., Brauer, M., ... Zhang, Q., 2017. Anthropogenic fugitive, combustion and industrial dust is a significant, underrepresented fine particulate matter source in global atmospheric models. Environmental Research Letters 12 (4), 044018. Pope III, C.A., Dockery, D.W., 2006. Health effects of fine particulate air pollution: lines that connect. J. Air Waste Manage. Assoc. 56 (6), 709–742. Rashki, A., Kaskaoutis, D.G., Goudie, A.S., Kahn, R.A., 2013. Dryness of ephemeral lakes and consequences for dust activity: the case of the Hamoun drainage basin, southeastern Iran. Sci. Total Environ. 463, 552–564. Reese, B.K., Anderson, M.A., Amrhein, C., 2008. Hydrogen sulfide production and volatilization in a polymictic eutrophic saline lake, Salton Sea, California. Sci. Total Environ. 406 (1–2), 205–218. Reis, M., 2012. Food insecurity and the relationship between household income and children’s health and nutrition in Brazil. Health Econ. 21 (4), 405–427. SCAQMD, 2006. Final-Methodology to Calculate Particulate Matter (PM) 2.5 and PM 2.5 Significance Thresholds. South Coast Air Quality Management District, Diamond Bar, CA http://www.aqmd.gov/docs/default-source/ceqa/handbook/localized-significance-thresholds/particulate-matter-(pm)-2.5-significance-thresholds-and-calculation-methodology/final-methodology-to-calculate-pm2-5-and-pm2-5-significancethresholds.doc. SCAQMD, 2013. CAQMD Installs Hydrogen Sulfide Monitoring Network near Salton Sea. South Coast Air Quality Management District, Diamond Bar, CA URL. http://www. aqmd.gov/home/news-events/current-news/2013-news-archives/salton-sea-monitors-installed. State of California PEIR, 2007. Salton Sea Ecosystem Restoration Program, Volume 1: Final Programmatic Environmental Impact Report. Sacramento, CA. Accessed on 12/3/18. URL. https://water.ca.gov/saltonsea/. Sullivan, D.M., Krupnick, A., 2018. Using satellite data to fill the gaps in the US air pollution monitoring network. Resources for the Future Working Paper. URL. http://www.rff.

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org/valuables/research/publications/using-satellite-data-fill-gaps-us-air-pollutionmonitoring-network. Sullivan, A., White, D.D., Hanemann, M., 2019. Designing collaborative governance: insights from the drought contingency planning process for the lower Colorado River basin. Environ. Sci. Pol. 91, 39–49. Tao, S., Fang, J., Zhao, X., Zhao, S., Shen, H., Hu, H., ... Guo, Q., 2015. Rapid loss of lakes on the Mongolian Plateau. Proceedings of the National Academy of Sciences 112 (7), 2281–2286. Taylor, D.A., 2002. Dust in the wind. Environ. Health Perspect. 110 (2), A80. US Bureau of Reclamation, 1997. Salton Sea 1995 Hydrographic GPS Survey. US Bureau of Reclamation, Technical Service Center, Denver, CO Accessed on 11/30/18. URL. http:// www.sci.sdsu.edu/salton/saltcont.htm. US EPA, 2017. Owens Valley Particulate Matter Plan Q&A. US EPA, Pacific Southwest, Region 9 Accessed on 11/29/18. URL. https://19january2017snapshot.epa.gov/www3/ region9/air/owens/qa.html. US EPA, 2018. Mortality Risk Valuation. US EPA, Washington, DC Accessed on 12/6/18. URL. https://www.epa.gov/environmental-economics/mortality-risk-valuation. van Donkelaar, A., Martin, R.V., Spurr, R.J., Burnett, R.T., 2015. High-resolution satellitederived PM2.5 from optimal estimation and geographically weighted regression over North America. Environ. Sci. Technol. 49 (17), 10482–10491. van Donkelaar, A., Martin, R.V., Brauer, M., Hsu, N.C., Kahn, R.A., Levy, R.C., ... Winker, D.M., 2016. Global estimates of fine particulate matter using a combined geophysicalstatistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 50 (7), 3762–3772. van Donkelaar, A., Martin, R.V., Li, C., Burnett, R.T., 2019. Regional estimates of chemical composition of fine particulate matter using a combined geoscience-statistical method with information from satellites, models, and monitors. Environ. Sci. Technol. 53 (5), 2595–2611. Watson, J.G., Chow, J.C., 2001. Source characterization of major emission sources in the Imperial and Mexicali Valleys along the US/Mexico border. Sci. Total Environ. 276 (1–3), 33–47. WHO, 2007. Global Surveillance, Prevention and Control of Chronic Respiratory Diseases: A Comprehensive Approach. World Health Organization, Geneva, Switzerland Accessed on 12/3/18. URL. http://www.who.int/gard/publications/GARD%20Book% 202007.pdf. Wurtsbaugh, W.A., Miller, C., Null, S.E., DeRose, R.J., Wilcock, P., Hahnenberger, M., ... Moore, J., 2017. Decline of the world's saline lakes. Nature Geoscience 10 (11), 816. Xing, Y.F., Xu, Y.H., Shi, M.H., Lian, Y.X., 2016. The impact of PM2.5 on the human respiratory system. J. Thorac. Dis. 8 (1), E69. Zivin, J., Neidell, M., 2012. The impact of pollution on worker productivity. Am. Econ. Rev. 102 (7), 3652–3673. Zivin, J., Neidell, M., 2013. Environment, health, and human capital. J. Econ. Lit. 51 (3), 689–730.