Human activities are increasingly degrading ecosystems, resulting in habitat loss and reduced biodiversity. Extinction rates, a widely used metric of biodiversity loss, are estimated to be around 1000 times as high as those historically experienced on Earth (1); even the most conservative estimate puts current extinction rates at 114 times as high as the background planetary norm (2). Simulations project that, under scenarios of increased economic growth and the accompanying land use change, critical habitat will further degrade and biodiversity will decline (3, 4). Are economic growth and ecosystem conservation incompatible objectives?
Exposure to particulate matter (PM10) has been associated with increased morbidity
and mortality. However, since pollution is correlated with confounding factors that
might otherwise affect health, identifying the causal link has proven challenging. This
study exploits the timing of sandstorms, which leads to a short-term increase in PM10,
to quantify the causal effect of PM10 increases on hospital admissions.
OBJECTIVE – This study uses the instrumental variable methodology in order to
control for confounding factors affecting hospital admissions. This allows for a better
estimate of the relationship between PM10 and hospital admissions.
Data on particulate matter concentrations and hospital admissions rates were compiled
for Israel’s two largest cities, Jerusalem and Tel Aviv, for 2007-2009.
We estimate using instrumental variables (IV) the impact of PM10 on hospital
admissions, exploiting the timing of sandstorms. We compare our IV estimates to those
derived from a Poisson regression, which is commonly used in the existing literature.
Sandstorms lead to an increase of 307 micrograms of PM10 concentrations and that a
10 microgram increase in PM10 is associated with a 0.8% (1%) increase in hospital
admissions due to respiratory conditions, using Poisson regression (IV).
The association between PM10 and hospital admission reflects a primarily causal
relationship. Instrumental variable methodology could be applied to the analysis of the
effect of air pollution on hospital admissions. The results estimates are in the same order
of magnitude as the standard Poisson regression analyses. However, the instrumental
variable approach is potentially better in identifying the causal link between air
pollution and hospital admissions.
The location of tide gauges is not random. If their locations are positively (negatively) correlated with sea level rise (SLR), estimates of global SLR will be biased upwards (downwards). Using individual tide gauges obtained from the Permanent Service for Mean Sea Level during 1807–2010, we show that tide gauge locations in 2000 were independent of SLR as measured by satellite altimetry. Therefore these tide gauges constitute a quasi-random sample, and inferences about global SLR obtained from them are unbiased. Using recently developed methods for nonstationary time series, we find that sea levels rose in 7 % of tide gauge locations and fell in 4 %. The global mean increase is 0.39–1.03 mm/year. However, the mean increase for locations where sea levels are rising is 3.55–4.42 mm/year. These findings are much lower than estimates of global sea level (2.2 mm/year) reported in the literature and adopted by IPCC (2014), and which make widespread use of imputed data for locations which do not have tide gauges. We show that although tide gauge locations in 2000 are uncorrelated with SLR, the global diffusion of tide gauges during the 20th century was negatively correlated with SLR. This phenomenon induces positive imputation bias in estimates of global mean sea levels because tide gauges installed in the 19th century happened to be in locations where sea levels happened to be rising.