Potential overall heat exposure reduction associated with implementation of heat mitigation strategies in Los Angeles.

We analyzed two historical extreme heat events in Los Angeles to explore the potential of increasing vegetative cover and surface solar reflectance (albedo) to reduce total exposure (indoor and outdoor) to dangerously hot conditions. We focus on three population subgroups, the elderly, office workers, and outdoor workers, and explore the extreme case where each subgroup does not have functioning air conditioning in their residences. For each heat event, we conducted atmospheric model simulations for a control case and four mitigation cases with varying levels of increased albedo and vegetation cover. Simultaneously, we conducted building simulations of representative residential buildings that lacked mechanical air conditioning. These simulations factored in both the indirect cooling effects associated with neighborhood implementation of mitigation strategies and the direct effects of high albedo roofing on the individual buildings. From both the atmospheric and building models, we exported hourly values of air temperature and dew point temperature, and used this information in combination with various scenarios of occupant behavior to create profiles of individual heat exposure. We also gathered heat-mortality data for the two heat events and developed a synoptic climatology-based relationship between exposure and excess mortality. This relationship was then applied to the scenarios in which albedo and canopy cover were increased. The results suggest that improvements in indoor thermal conditions are responsible for a sizable portion of the health benefit of large-scale implementation of heat mitigation strategies.

Association of weather and air pollution interactions on daily mortality in 12 Canadian cities.

It has been well established that both meteorological attributes and air pollution concentrations affect human health outcomes. We examined all cause nonaccident mortality relationships for 28 years (1981-2008) in relation to air pollution and synoptic weather type (encompassing air mass) data in 12 Canadian cities. This study first determines the likelihood of summertime extreme air pollution events within weather types using spatial synoptic classification. Second, it examines the modifying effect of weather types on the relative risk of mortality (RR) due to daily concentrations of air pollution (nitrogen dioxide, ozone, sulfur dioxide, and particulate matter <2.5 µm). We assess both single- and two-pollutant interactions to determine dependent and independent pollutant effects using the relatively new time series technique of distributed lag nonlinear modeling (DLNM). Results display dry tropical (DT) and moist tropical plus (MT+) weathers to result in a fourfold and twofold increased likelihood, respectively, of an extreme pollution event (top 5 % of pollution concentrations throughout the 28 years) occurring. We also demonstrate statistically significant effects of single-pollutant exposure on mortality (p < 0.05) to be dependent on summer weather type, where stronger results occur in dry moderate (fair weather) and DT or MT+ weather types. The overall average single-effect RR increases due to pollutant exposure within DT and MT+ weather types are 14.9 and 11.9 %, respectively. Adjusted exposures (two-way pollutant effect estimates) generally results in decreased RR estimates, indicating that the pollutants are not independent. Adjusting for ozone significantly lowers 67 % of the single-pollutant RR estimates and reduces model variability, which demonstrates that ozone significantly controls a portion of the mortality signal from the model. Our findings demonstrate the mortality risks of air pollution exposure to differ by weather type, with increased accuracy obtained when accounting for interactive effects through adjustment for dependent pollutants using a DLNM.

Synoptic climatology of the long-distance dispersal of white pine blister rust. I. Development of an upper level synoptic classification.

This study developed a methodology to temporally classify large scale, upper level atmospheric conditions over North America, utilizing a newly-developed upper level synoptic classification (ULSC). Four meteorological variables: geopotential height, specific humidity, and u- and v-wind components, at the 500 hPa level over North America were obtained from the NCEP/NCAR Reanalysis Project dataset for the period 1965-1974. These data were subjected to principal components analysis to standardize and reduce the dataset, and then an average linkage clustering algorithm identified groups of observations with similar flow patterns. The procedure yielded 16 clusters. These flow patterns identified by the ULSC typify all patterns expected to be observed over the study area. Additionally, the resulting cluster calendar for the period 1965-1974 showed that the clusters are generally temporally continuous. Subsequent classification of additional observations through a z-score method produced acceptable results, indicating that additional observations may easily be incorporated into the ULSC calendar. The ULSC calendar of synoptic conditions can be used to identify situations that lead to periods of extreme weather, i.e., heat waves, flooding and droughts, and to explore long-distance dispersal of airborne particles and biota across North America.

Synoptic climatology of the long-distance dispersal of white pine blister rust II. Combination of surface and upper-level conditions.

An invasive forest pathogen, Cronartium ribicola, white pine blister rust (WPBR), is believed to have arrived in the Sacramento Mountains of south-central New Mexico about 1970. Epidemiological and genetic evidence supports the hypothesis that introduction was the result of long-distance dispersal (LDD) by atmospheric transport from California. This study applies a method to identify the atmospheric conditions favorable for rust transport and infection. An upper level synoptic classification (ULSC) identifies patterns of upper-level flow favorable for the transport of rust spores from a source to a target. Transport data are coupled with data for surface conditions favorable for infection at a designated target. A resulting calendar lists likelihood classes for establishment by four-times-daily observations during a dispersal season from April through July in the years 1965 to 1974. The single most-favorable period for transport and infection at the New Mexico site was identified as 1-15 June 1969. Five additional sites in the western United States with susceptible white pine populations and known infestation status were then evaluated to verify the model. Only the infested sites exhibit an establishment likelihood of "high" or "very high." This suggests that the methodology correctly identifies locations with elevated establishment likelihood. Finally, likelihoods at nine additional points in the southwestern United States are determined and used to map regional patterns of transport, infection and establishment. The ULSC combined with appropriate surface meteorological data could be used to further investigate transport and infection, identify other areas at risk, assess the potential for gene flow of WPBR and evaluate long-distance dispersal of other pathogens.

An operational heat/health warning system in Shanghai.

Previous research has noted that high surface temperatures within certain "offensive" air masses can lead to increased mortality. This study assesses the relationship between daily mortality rates and weather within the city of Shanghai, China, while introducing an operational heat/health warning system for the city. Using numerous meteorological observations, the spatial synoptic classification has been used to classify each summer day from 1989 to 1998 into one of eight air mass types for Shanghai. Through the comparative analysis of the daily air mass type and the corresponding Shanghai mortality rate, "moist tropical plus" (MT+), an extremely hot and humid air mass, was identified as an offensive air mass with the highest rates of mortality. Using stepwise regression, an algorithm was produced to help predict the number of excess deaths that will occur with each occurrence of the MT+ airmass. The heat/health warning system was run experimentally in the summer of 2001 and illustrated that the use of a warning system can alert the city's residents of potentially offensive weather situations that can lead to a deterioration in human health.

Predicting key malaria transmission factors, biting and entomological inoculation rates, using modelled soil moisture in Kenya.

While malaria transmission varies seasonally, large inter-annual heterogeneity of malaria incidence occurs. Variability in entomological parameters, biting rates and entomological inoculation rates (EIR) have been strongly associated with attack rates in children. The goal of this study was to assess the weather's impact on weekly biting and EIR in the endemic area of Kisian, Kenya. Entomological data collected by the U.S. Army from March 1986 through June 1988 at Kisian, Kenya was analysed with concurrent weather data from nearby Kisumu airport. A soil moisture model of surface-water availability was used to combine multiple weather parameters with landcover and soil features to improve disease prediction. Modelling soil moisture substantially improved prediction of biting rates compared to rainfall; soil moisture lagged two weeks explained up to 45% of An. gambiae biting variability, compared to 8% for raw precipitation. For An. funestus, soil moisture explained 32% variability, peaking after a 4-week lag. The interspecies difference in response to soil moisture was significant (P < 0.00001). A satellite normalized differential vegetation index (NDVI) of the study site yielded a similar correlation (r = 0.42 An. gambiae). Modelled soil moisture accounted for up to 56% variability of An. gambiae EIR, peaking at a lag of six weeks. The relationship between temperature and An. gambiae biting rates was less robust; maximum temperature r2 = -0.20, and minimum temperature r2 = 0.12 after lagging one week. Benefits of hydrological modelling are compared to raw weather parameters and to satellite NDVI. These findings can improve both current malaria risk assessments and those based on El Niño forecasts or global climate change model projections.

New procedures to estimate water temperatures and water depths for application in climate-dengue modeling.

Two new approaches have been developed to estimate water temperatures and water depths in containers that commonly are used as breeding sites for mosquitoes, the primary vectors of dengue viruses. These estimates are incorporated in recently developed stochastic simulation models used to describe the daily dynamics of dengue virus transmission in the urban environment. Water temperature estimates are provided through a regression model that includes meteorological variables not previously used; results show that they are significantly better than those used in previous dengue transmission models. Water depth models use a climatic water budget approach which estimates moisture storage within containers. The water depth models are less precise than those developed for water temperature; however, results are superior to those used in previous models. These new approaches should improve estimates of the impact of water conditions on dengue vectors.

Does weather confound or modify the association of particulate air pollution with mortality? An analysis of the Philadelphia data, 1973-1980.

Because weather has the potential to confound or modify the pollution-mortality relationship, researchers have developed several approaches for controlling it in estimating the independent effect of air pollution on mortality. This report considers the consequences of using alternative approaches to controlling for weather and explores modification of air pollution effects by weather, as weather patterns could plausibly alter air pollution's effect on health. We analyzed 1973-1980 total mortality data for Philadelphia using four weather models and compared estimates of the effects of TSP and SO2 on mortality using a Poisson regression model. Two synoptic categories developed by Kalkstein were selected--the Temporal Synoptic Index (TSI) and the Spatial Synoptic Classification (SSC)--and compared with (1) descriptive models developed by Schwartz and Dockery (S-D); and (2) LOESS, a non-parametric function of the previous day's temperature and dew point. We considered model fit using Akaike's Information Criterion (AIC) and changes in the estimated effects of TSP and SO2. In the full-year analysis, S-D is better than LOESS at predicting mortality, and S-D and LOESS are better than TSI, as measured by AIC. When TSP or SO2 was fit alone, the results were qualitatively similar, regardless of how weather was controlled; when TSP and SO2 were fit simultaneously, the S-D and LOESS models give qualitatively different results than TSI, which attributes more of the pollution effect to SO2 than to TSP. Model fit is substantially poorer with TSI. This pattern was repeated in analyses of summer and winter months, which included SSC. In summary, using synoptic weather categories in regression models does not meaningfully change the association between mortality and air pollution indexes. We also found little evidence that weather conditions modified the effect of pollution, regardless of the approach used to represent weather.

A synoptic evaluation of asthma hospital admissions in New York City.

An evaluation of weather/asthma relationships in the New York City Standard Metropolitan Statistical Area (SMSA) is developed using a synoptic climatological methodology. This procedure isolates "air masses," or bodies of air that are homogeneous in meteorological character, and relates them to daily counts of overnight asthma hospital admissions. The synoptic procedure used here, known as the temporal synoptic index (TSI), can identify air masses in automated fashion for every day over many years. It is apparent that certain air masses are related to statistically significant increases in asthma hospital admissions. The impact varies seasonally, with weather having a particularly important impact on asthma admissions during fall and winter. It appears that air pollution has little impact on asthma during these two seasons, and the air masses associated with the highest admissions are not distinguished by high concentrations of pollutants. However, during spring and summer, the air masses associated with highest admissions are among those with high pollution concentrations. There is a strong interseasonal differential response to weather and air pollution by asthmatics in New York City. If these results can be replicated at other locations in future studies, it may be possible to develop an asthma/weather watch-warning system, based on the expected arrival of high-admissions air masses.

An evaluation of climate/mortality relationships in large U.S. cities and the possible impacts of a climate change.

A new air mass-based synoptic procedure is used to evaluate climate/mortality relationships as they presently exist and to estimate how a predicted global warming might alter these values. Forty-four large U.S. cities with metropolitan areas exceeding 1 million in population are analyzed. Sharp increases in mortality are noted in summer for most cities in the East and Midwest when two particular air masses are present. A very warm air mass of maritime origin is most important in the eastern United States, which when present can increase daily mortality by as many as 30 deaths in large cities. A hot, dry air mass is important in many cities, and, although rare in the East, can increase daily mortality by up to 50 deaths. Cities in the South and Southwest show lesser weather/mortality relationships in summer. During winter, air mass-induced increases in mortality are considerably less than in summer. Although daily winter mortality is usually higher than summer, the causes of death that are responsible for most winter mortality do not vary much with temperature. Using models that estimate climate change for the years 2020 and 2050, it is estimated that summer mortality will increase dramatically and winter mortality will decrease slightly, even if people acclimatize to the increased warmth. Thus, a sizable net increase in weather-related mortality is estimated if the climate warms as the models predict.

Synoptic weather modeling and estimates of the exposure-response relationship between daily mortality and particulate air pollution.

This study estimated the association between particulate air pollution and daily mortality in Utah Valley using the synoptic climatological approach to control for potential weather effects. This approach was compared with alternative weather modeling approaches. Although seasonality explained a significant amount of variability in mortality, other weather variables explained only a very small amount of additional variability in mortality. The synoptic climatological approach performed as well or slightly better than alternative approaches to controlling for weather. However, the estimated effect of particulate pollution on mortality was mostly unchanged or slightly larger when synoptic categories were used to control for weather. Furthermore, the shape of the estimated dose-response relationship was similar when alternative approaches to controlling for weather were used. The associations between particulate pollution and daily mortality were not significantly different from a linear exposure-response relationship that extends throughout the full observed range of pollution.

The impact of climate change on human health: some international implications.

The objective of this study is to discuss the potential impact of a global warming on various aspects of human health. Changes in heat-related mortality are estimated for four countries: the United States, Canada, the People's Republic of China and Egypt. In addition, the potential confounding impact of increased air pollution is considered. Finally, a framework to analyze two vector-borne diseases, onchocerciasis and malaria, which may spread if temperatures increase, is discussed. Our findings suggest that heat-related mortality is estimated to rise significantly in all four countries if the earth warms, with the greatest impacts in China and Egypt. The most sensitive areas are those with intense but irregular heat waves. In the United States, air pollution does not appear to impact daily mortality significantly when severe weather is present, although it seems to have a slight influence when weather conditions are not stressful.

A new approach to evaluate the impact of climate on human mortality.

The objective of this study is to introduce a new procedure to determine the impact of climate on human mortality with the use of a synoptic climatological approach. The holistic nature of synoptic categories allows for the simultaneous evaluation of numerous weather elements as they realistically appear within air masses. In addition, this approach allows for a better distinction between pollution-induced mortality and weather-induced mortality. A synoptic categorization was performed for St. Louis, Missouri, and each category was evaluated in terms of its mean daily mortality. Of 10 summer categories found in St. Louis, one possessed the highest mean mortality by far, and 8 of the top 10 mortality days in St. Louis occurred when this category was present. Further analysis determined that long, consecutive day periods of this hot, oppressive category are associated with a continuing rise in mortality. It was determined that the procedure described here has the potential to be used in a weather/mortality watch-warning system. Finally, it appears that day-to-day mortality fluctuations are much more sensitive to weather than to pollution concentrations, as the oppressive category associated with the greatest mortality possessed levels of six major pollutants that were not noteworthy.