Effect of Precipitation Variability on Pollutant Concentrations in Soil: Implications for Exposure and Risk Assessments

Concentrations of toxic pollutants in surface soils must be predicted in order to assess exposures and risks that may arise from emissions at incinerators and other air pollution sources. At present, concentrations are predicted using deterministic models and time-averaged values of input parameters. This steady-state equilibrium approach does not address variability in the underlying transport and fate processes. This paper explores the variability of pollutant concentrations in surface soils that arises from precipitation, an intermittent process that governs wet deposition and leaching processes. Using long-term (45 to 50 years) historical records at 6 climato-logically diverse sites, concentrations predicted using the steady-state approach are compared to those predicted using a dynamic numerical model that simulates dry and wet deposition, leaching, and pollutant accumulation in the surface layer of soil using a daily time step. The models are compared for pollutants of low, medium, and high water solubility. Both models show that predictions depend strongly on the pollutant solubility and the precipitation pattern at the location. Average concentrations differed between locations by a factor of up to 4 due to precipitation patterns; the solubility of the pollutant had a much more pronounced effect. Both models produced similar long-term trends, for example, the duration of the period needed to achieve a quasi-steady-state pollutant concentration. However, for soluble pollutants, the dynamic model produced maximum 24-hr average concentrations that exceeded long-term averages by 4 to 8 times, and long-term predictions of the dynamic model exceeded predictions of the steady-state model by 1.9 to 3.6 times (depending on the site). These differences are caused by the steady-state assumption that deposition and leaching occur continuously. While the steady-state model can be used to estimate long-term trends of moderately to highly insoluble pollutants, the dynamic model should be used to predict short-term, maximum concentrations and both short- and long-term averages of soluble pollutants. Site-specific exposure and risk assessments should consider temporal variation and the use of a dynamic model if concentrations of soluble pollutants approach risk-based target levels.     

Probability and Persistence of High Pollutant Concentrations in Soils: A Modeling Study and Implications for Exposure and Risk Assessment

Risk assessments for environmental pollutants have relied upon steady-state models that do not represent the variability of pollutant transport and fate processes, thus predictions are unlikely to reflect the true variability in pollutant concentrations. Such models cannot be used to estimate the probability, magnitude and duration of short- to intermediate-term and high-concentration events that might lead to adverse acute impacts. In this study, a numerical model is used to simulate pollutant accumulation in surface soils at six U.S. locations that result from atmospheric deposition and leaching. Historical (50 year) precipitation data drive the model. Model predictions are filtered and analyzed to identify high pollution events (exceeding specific concentration thresholds) and their occurrence probability and duration. Predicted concentrations at each site varied by a factor of 100 over time and by a factor of five among the six locations. The frequency and duration of high pollution events also differed by locations and concentration threshold. In general, larger thresholds lead to less frequent events and shorter durations. The proposed method allows estimates of the probability of occurrence and duration of high pollution events, providing information that complements the steady-state methods.     

A measurement error model for time-series studies of air pollution and mortality

One barrier to interpreting the observational evidence concerning the adverse health effects of air pollution for public policy purposes is the measurement error inherent in estimates of exposure based on ambient pollutant monitors. Exposure assessment studies have shown that data from monitors at central sites may not adequately represent personal exposure. Thus, the exposure error resulting from using centrally measured data as a surrogate for personal exposure can potentially lead to a bias in estimates of the health effects of air pollution. This paper develops a multi-stage Poisson regression model for evaluating the effects of exposure measurement error on estimates of effects of particulate air pollution on mortality in time-series studies. To implement the model, we have used five validation data sets on personal exposure to PM10. Our goal is to combine data on the associations between ambient concentrations of particulate matter and mortality for a specific location, with the validation data on the association between ambient and personal concentrations of particulate matter at the locations where data have been collected. We use these data in a model to estimate the relative risk of mortality associated with estimated personal-exposure concentrations and make a comparison with the risk of mortality estimated with measurements of ambient concentration alone. We apply this method to data comprising daily mortality counts, ambient concentrations of PM10measured at a central site, and temperature for Baltimore, Maryland from 1987 to 1994. We have selected our home city of Baltimore to illustrate the method; the measurement error correction model is general and can be applied to other appropriate locations.Our approach uses a combination of: (1) a generalized additive model with log link and Poisson error for the mortality-personal-exposure association; (2) a multi-stage linear model to estimate the variability across the five validation data sets in the personal-ambient-exposure association; (3) data augmentation methods to address the uncertainty resulting from the missing personal exposure time series in Baltimore. In the Poisson regression model, we account for smooth seasonal and annual trends in mortality using smoothing splines. Taking into account the heterogeneity across locations in the personal-ambient-exposure relationship, we quantify the degree to which the exposure measurement error biases the results toward the null hypothesis of no effect, and estimate the loss of precision in the estimated health effects due to indirectly estimating personal exposures from ambient measurements.     

Stochastic simulation of daily air temperature and precipitation from monthly normals in North America north of Mexico

A simple, stochastic daily temperature and precipitation generator (TEMPGEN) was developed to generate inputs for the study of the effects of climate change on models driven by daily weather information when climate data are available as monthly summaries. The model uses as input only 11 sets of monthly normal statistics from individual weather stations. It needs no calibration, and was parameterized and validated for use in Canada and the continental United States. Monthly normals needed are: mean and standard deviation of daily minimum and maximum temperature, first and second order autoregressive terms for daily deviations of minimum and maximum temperatures from their daily means, correlation of deviations of daily minimum and maximum temperatures, total precipitation, and the interannual variance of total precipitation. The statistical properties and distributions of daily temperature and precipitation data produced by this generator compared quite favorably with observations from 708 stations throughout North America (north of Mexico). The algorithm generates realistic seasonal patterns, variability and extremes of temperature, precipitation, frost-free periods and hot spells. However, it predicts less accurately the daily probability of precipitation, extreme precipitation events and the duration of extreme droughts.     

Normal and Extreme Wind Conditions for Power at Coastal Locations in China

In this paper, the normal and extreme wind conditions for power at 12 coastal locations along China’s coastline were investigated. For this purpose, the daily meteorological data measured at the standard 10-m height above ground for periods of 40–62 years are statistically analyzed. The East Asian Monsoon that affects almost China’s entire coastal region is considered as the leading factor determining wind energy resources. For most stations, the mean wind speed is higher in winter and lower in summer. Meanwhile, the wind direction analysis indicates that the prevalent winds in summer are southerly, while those in winter are northerly. The air densities at different coastal locations differ significantly, resulting in the difference in wind power density. The Weibull and lognormal distributions are applied to fit the yearly wind speeds. The lognormal distribution performs better than the Weibull distribution at 8 coastal stations according to two judgement criteria, the Kolmogorov–Smirnov test and absolute error (AE). Regarding the annual maximum extreme wind speed, the generalized extreme value (GEV) distribution performs better than the commonly-used Gumbel distribution. At these southeastern coastal locations, strong winds usually occur in typhoon season. These 4 coastal provinces, that is, Guangdong, Fujian, Hainan, and Zhejiang, which have abundant wind resources, are also prone to typhoon disasters.     

延安市安塞区极端降水变化特征

以1980—2019年安塞气象站的日降水资料为基础选取11个极端降水指标,从降水强度和降水频率的角度对安塞区的极端降水数据进行分析,并对未来极端降水趋势进行预测。结果表明: 1980—2019年,安塞区极端降水指标总体呈现下降趋势,其中,大雨以上日数和普通日降水强度的下降趋势达到显著水平,其气候倾向斜率分别为-0.65 d·(10 a)^-1和-0.32 mm·d^-1·(10 a)^-1。除持续湿润日数和极端降水总量外,其余极端降水指标均存在突变点,突变之后各指标多呈下降趋势,且年降水量、中雨以上日数、大雨以上日数、暴雨以上日数、异常降水总量、普通日降水强度的下降趋势达到显著水平。持续干燥日数与其他指标的相关性较低且与一些指标呈负相关,而持续湿润日数只与少数几个指标具有相关性,此外,其他极端降水指标之间均呈显著相关。Hurst指数分析表明,未来安塞区极端降水总体变化趋势具有持续性。     

气候变化对淮河流域中上游汛期极端流量影响的SWAT模拟

致洪暴雨主要是3天以上连续强降水,是淮河流域洪涝的直接原因。构建淮河流域中上游SWAT模型,用RegCM3在SRES A2排放情景下的模拟结果(2071-2100年)驱动SWAT模型,研究气候变化对淮河流域中上游汛期极端流量的影响。结果表明:(1)在SRES A2排放情景下,淮河流域中上游未来(2071-2100年)气温升高,降水量增加,降水的空间差异增大;颖河流域中游年降水量有较大幅度的减少,呈现暖干化的趋势;汛期极端过程降水增加,汛期最大9 d降水量平均增幅都在10%以上。(2)在SRES A2排放情景下的气候变化将导致淮河流域中上游汛期极端流量大幅度增加,干流5个水文站汛期最大9 d平均流量的增幅都在20%以上。(3)淮河流域中上游极端流量的概率分布更加集中,更大的极端流量出现的频率更高,研究流域下游更容易出现较大的极端流量。(4)研究流域下游极端流量概率对极端流量变化更敏感,下游也面临着更大的洪涝风险。     

Historical precipitation predictably alters the shape and magnitude of microbial functional response to soil moisture

Soil moisture constrains the activity of decomposer soil microorganisms, and in turn the rate at which soil carbon returns to the atmosphere. While increases in soil moisture are generally associated with increased microbial activity, historical climate may constrain current microbial responses to moisture. However, it is not known if variation in the shape and magnitude of microbial functional responses to soil moisture can be predicted from historical climate at regional scales. To address this problem, we measured soil enzyme activity at 12 sites across a broad climate gradient spanning 442–887 mm mean annual precipitation. Measurements were made eight times over 21 months to maximize sampling during different moisture conditions. We then fit saturating functions of enzyme activity to soil moisture and extracted half saturation and maximum activity parameter values from model fits. We found that 50% of the variation in maximum activity parameters across sites could be predicted by 30‐year mean annual precipitation, an indicator of historical climate, and that the effect is independent of variation in temperature, soil texture, or soil carbon concentration. Based on this finding, we suggest that variation in the shape and magnitude of soil microbial response to soil moisture due to historical climate may be remarkably predictable at regional scales, and this approach may extend to other systems. If historical contingencies on microbial activities prove to be persistent in the face of environmental change, this approach also provides a framework for incorporating historical climate effects into biogeochemical models simulating future global change scenarios.     

气候变化背景下中国农业气候资源变化Ⅵ.黑龙江省三江平原地区降水资源变化特征及其对春玉米生产的可能影响

利用黑龙江省三江平原地区1959-2007年降水资料和1983-2007年春玉米生育期资料,采用百分位法确定了各站点的极端降水阈值,结合极端降水频次、强度、最长连续(无)降水日数、极端降水贡献率等指标,分析了三江平原地区极端降水的年际间变化特征、不同等级的降水量变化以及春玉米各生育阶段极端降水的分配特征.结果表明:1959-2007年间,研究区域年降水量呈略微减少趋势,且年降水日数的减幅远大于降水量,年内降水量分布更趋于集中;极端降水频次和强度均呈减少趋势,极端降水频次的年际间波动大于极端降水强度;年极端降水量占全年降水量的比例略有减少,减少趋势不显著;年小雨日数极显著减少,而年中雨日数和年内大到暴雨日数的减少趋势不显著.三江平原地区春玉米各生育阶段的极端降水分配比例由高到低依次为生殖生长阶段、营养生长与生殖生长并存阶段、营养生长阶段和出苗前;春玉米生长季内降水量占年降水量的比例显著减少,导致春玉米生长季缺水的风险加大;春玉米生长季内最长连续无降水日数呈极显著增加趋势,增幅达1.1 d·(10 a)-1,而最长连续降水日数却呈极显著下降趋势,减幅为0.5 d·(10 a)-1,说明研究区自然降水条件下春玉米生长季干旱风险有所加大.     

Changes of China agricultural climate resources under the background of global climate change. VI. Change characteristics of precipitation resource and its possible effect on maize production in Sanjiang Plain of Heilongjiang Province

Based on the 1959-2007 daily precipitation data and 1983-2007 spring maize phenologyical data, the thresholds of extreme precipitation at different places in Sanjiang Plain of Heilongjiang Province were calculated by percentile method, and, in combining with the indices involving the frequency and intensity of extreme precipitation, longest consecutive wet (dry) days, and contribution rate of extreme precipitation, the annual change characteristics of extreme precipitation, quantitative change of different grade precipitation, and distribution characteristics of extreme precipitation at each growth stage of spring maize were analyzed. In 1959-2007, the annual precipitation in Sanjiang Plain showed a slight decreasing trend, and the decreasing amplitude of precipitation days was much larger than that of precipitation. Accordingly, the annual distribution of precipitation tended to be more concentrated. The frequency and intensity of extreme precipitation declined, and the annual fluctuation of the frequency was bigger than that of the intensity. There was a slight decrease in the proportion of annual extreme precipitation to annual precipitation, but the decreasing tendency was not significant. The annual light rain days had a significant decreasing trend, but the annual moderate and heavy rain days didn't have. During spring maize growth season, the distribution ratio of extreme precipitation from high to low was reproductive growth stage, coexistence stage of vegetative growth and reproductive growth, vegetative growth stage, and premergence stage. There was a significant decrease in the proportion of the precipitation during spring maize growth season to annual precipitation, resulting in an increasing risk of precipitation scarcity during the growth season. The longest consecutive dry days during spring maize growth season showed a significant increasing trend, with the increment averaged 1.1 d x (10a)(-1), while the longest consecutive wet days showed a significant decreasing trend, with the decrement averaged 0.5 d x (10a)(-1). Under natural precipitation, the spring maize drought risk in the study region increased.     

Range position and climate sensitivity: The structure of among‐population demographic responses to climatic variation

Species’ distributions will respond to climate change based on the relationship between local demographic processes and climate and how this relationship varies based on range position. A rarely tested demographic prediction is that populations at the extremes of a species’ climate envelope (e.g., populations in areas with the highest mean annual temperature) will be most sensitive to local shifts in climate (i.e., warming). We tested this prediction using a dynamic species distribution model linking demographic rates to variation in temperature and precipitation for wood frogs (Lithobates sylvaticus) in North America. Using long‐term monitoring data from 746 populations in 27 study areas, we determined how climatic variation affected population growth rates and how these relationships varied with respect to long‐term climate. Some models supported the predicted pattern, with negative effects of extreme summer temperatures in hotter areas and positive effects on recruitment for summer water availability in drier areas. We also found evidence of interacting temperature and precipitation influencing population size, such as extreme heat having less of a negative effect in wetter areas. Other results were contrary to predictions, such as positive effects of summer water availability in wetter parts of the range and positive responses to winter warming especially in milder areas. In general, we found wood frogs were more sensitive to changes in temperature or temperature interacting with precipitation than to changes in precipitation alone. Our results suggest that sensitivity to changes in climate cannot be predicted simply by knowing locations within the species’ climate envelope. Many climate processes did not affect population growth rates in the predicted direction based on range position. Processes such as species‐interactions, local adaptation, and interactions with the physical landscape likely affect the responses we observed. Our work highlights the need to measure demographic responses to changing climate.     

气候变化背景下中国农业气候资源变化Ⅵ. 黑龙江省三江平原地区降水资源变化特征及其对春玉米生产的可能影响

利用黑龙江省三江平原地区1959—2007年降水资料和1983—2007年春玉米生育期资料,采用百分位法确定了各站点的极端降水阈值,结合极端降水频次、强度、最长连续（无）降水日数、极端降水贡献率等指标,分析了三江平原地区极端降水的年际间变化特征、不同等级的降水量变化以及春玉米各生育阶段极端降水的分配特征.结果表明：1959—2007年间,研究区域年降水量呈略微减少趋势,且年降水日数的减幅远大于降水量,年内降水量分布更趋于集中;极端降水频次和强度均呈减少趋势,极端降水频次的年际间波动大于极端降水强度;年极端降水量占全年降水量的比例略有减少,减少趋势不显著;年小雨日数极显著减少,而年中雨日数和年内大到暴雨日数的减少趋势不显著.三江平原地区春玉米各生育阶段的极端降水分配比例由高到低依次为生殖生长阶段、营养生长与生殖生长并存阶段、营养生长阶段和出苗前;春玉米生长季内降水量占年降水量的比例显著减少,导致春玉米生长季缺水的风险加大;春玉米生长季内最长连续无降水日数呈极显著增加趋势,增幅达1.1 d·(10 a) ^-1,而最长连续降水日数却呈极显著下降趋势,减幅为0.5 d·(10 a)^-1,说明研究区自然降水条件下春玉米生长季干旱风险有所加大.     

Climate change and annual survival in a temperate passerine: partitioning seasonal effects and predicting future patterns

Predicting climate change impacts on population size requires detailed understanding of how climate influences key demographic rates, such as survival. This knowledge is frequently unavailable, even in well‐studied taxa such as birds. In temperate regions, most research into climatic effects on annual survival in resident passerines has focussed on winter temperature. Few studies have investigated potential precipitation effects and most assume little impact of breeding season weather. We use a 19‐year capture–mark–recapture study to provide a rare empirical analysis of how variation in temperature and precipitation throughout the entire year influences adult annual survival in a temperate passerine, the long‐tailed tit Aegithalos caudatus. We use model averaging to predict longer‐term historical survival rates, and future survival until the year 2100. Our model explains 73% of the interannual variation in survival rates. In contrast to current theory, we find a strong precipitation effect and no effect of variation in winter weather on adult annual survival, which is correlated most strongly to breeding season (spring) weather. Warm springs and autumns increase annual survival, but wet springs reduce survival and alter the form of the relationship between spring temperature and annual survival. There is little evidence for density dependence across the observed variation in population size. Using our model to estimate historical survival rates indicates that recent spring warming has led to an upward trend in survival rates, which has probably contributed to the observed long‐term increase in the UK long‐tailed tit population. Future climate change is predicted to further increase survival, under a broad range of carbon emissions scenarios and probabilistic climate change outcomes, even if precipitation increases substantially. We demonstrate the importance of considering weather over the entire annual cycle, and of considering precipitation and temperature in combination, in order to develop robust predictive models of demographic responses to climate change. Synthesis Prediction of climate change impacts demands understanding of how climate influences key demographic rates. In our 19‐year mark‐recapture study of long‐tailed tits Aegithalos caudatus, weather explained 73% of the inter‐annual variation in adult survival; warm springs and autumns increased survival, wet springs reduced survival, but winter weather had little effect. Robust predictions thus require consideration of the entire annual cycle and should not focus solely on temperature. Unexpectedly, survival appeared not to be strongly density‐dependent, so we use historical climate data to infer that recent climate change has enhanced survival over the four decades in which the UK long‐tailed tit population has more than doubled. Furthermore, survival rates in this species are predicted to further increase under a wide range of future climate scenarios.     

Can the past predict the future? Experimental tests of historically based population models

A frequently advocated approach for forecasting the population‐level impacts of climate change is to project models based on historical, observational relationships between climate and demographic rates. Despite the potential pitfalls of this approach, few historically based population models have been experimentally validated. We conducted a precipitation manipulation experiment to test population models fit to observational data collected from the 1930s to the 1970s for six prairie forb species. We used the historical population models to predict experimental responses to the precipitation manipulations, and compared these predictions to ones generated by a statistical model fit directly to the experimental data. For three species, a sensitivity analysis of the effects of precipitation and grass cover on forb population growth showed consistent results for the historical population models and the contemporary statistical models. Furthermore, the historical population models predicted population growth rates in the experimental plots as well or better than the statistical models, ignoring variation explained by spatial random effects and local density‐dependence. However, for the remaining three species, the sensitivity analyses showed that the historical and statistical models predicted opposite effects of precipitation on population growth, and the historical models were very poor predictors of experimental responses. For these species, historical observations were not well replicated in space, and for two of them the historical precipitation‐demography correlations were weak. Our results highlight the strengths and weaknesses of observational and experimental approaches, and increase our confidence in extrapolating historical relationships to predict population responses to climate change, at least when the historical correlations are strong and based on well‐replicated observations.     

Efficient measurement error correction with spatially misaligned data

Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.     

Tree growth variation in the tropical forest: understanding effects of temperature,rainfall and CO2

Tropical forest responses to climatic variability have important consequences for global carbon cycling, but are poorly understood. As empirical, correlative studies cannot disentangle the interactive effects of climatic variables on tree growth, we used a tree growth model (IBTREE) to unravel the climate effects on different physiological pathways and in turn on stem growth variation. We parameterized the model for canopy trees of Toona ciliata (Meliaceae) from a Thai monsoon forest and compared predicted and measured variation from a tree‐ring study over a 30‐year period. We used historical climatic variation of minimum and maximum day temperature, precipitation and carbon dioxide (CO₂) in different combinations to estimate the contribution of each climate factor in explaining the inter‐annual variation in stem growth. Running the model with only variation in maximum temperature and rainfall yielded stem growth patterns that explained almost 70% of the observed inter‐annual variation in stem growth. Our results show that maximum temperature had a strong negative effect on the stem growth by increasing respiration, reducing stomatal conductance and thus mitigating a higher transpiration demand, and – to a lesser extent – by directly reducing photosynthesis. Although stem growth was rather weakly sensitive to rain, stem growth variation responded strongly and positively to rainfall variation owing to the strong inter‐annual fluctuations in rainfall. Minimum temperature and atmospheric CO₂ concentration did not significantly contribute to explaining the inter‐annual variation in stem growth. Our innovative approach – combining a simulation model with historical data on tree‐ring growth and climate – allowed disentangling the effects of strongly correlated climate variables on growth through different physiological pathways. Similar studies on different species and in different forest types are needed to further improve our understanding of the sensitivity of tropical tree growth to climatic variability and change.     

Legacies of precipitation fluctuations on primary production: theory and data synthesis

Variability of above-ground net primary production (ANPP) of arid to sub-humid ecosystems displays a closer association with precipitation when considered across space (based on multiyear averages for different locations) than through time (based on year-to-year change at single locations). Here, we propose a theory of controls of ANPP based on four hypotheses about legacies of wet and dry years that explains space versus time differences in ANPP–precipitation relationships. We tested the hypotheses using 16 long-term series of ANPP. We found that legacies revealed by the association of current- versus previous-year conditions through the temporal series occur across all ecosystem types from deserts to mesic grasslands. Therefore, previous-year precipitation and ANPP control a significant fraction of current-year production. We developed unified models for the controls of ANPP through space and time. The relative importance of current-versus previous-year precipitation changes along a gradient of mean annual precipitation with the importance of current-year PPT decreasing, whereas the importance of previous-year PPT remains constant as mean annual precipitation increases. Finally, our results suggest that ANPP will respond to climate-change-driven alterations in water availability and, more importantly, that the magnitude of the response will increase with time.     

Downscaling climate parameters in Fars province,using models of the fifth report and RCP scenarios

Climate change is responsible for many extreme weather events on the Earth, including sea level rising, drastic shifts in temperature and precipitation regimes, and changes in flood and drought frequency. In the present study, based on IPCC's latest report, outputs of three GCMs, the EC-EARTH, the HadGEM2-ES and the MIROC5 were downscaled by the LARS-WG model and under RCP4.5 and RCP8.5 scenarios. Also, variations in precipitation, maximum and minimum temperatures for the time series 2021–2040, 2041–2060 and 2061–2080 have been projected and the precipitation extreme values in Gumbel distribution were evaluated. For this purpose, the climate records obtained from Shiraz, Lar and Abadeh synoptic stations in Fars province were used to establish the baseline period (1985–2010). The results of all the stations show that Changes in maximum and minimum temperatures under RCP4.5 and RCP8.5 scenarios have increased. The increase in minimum temperature for the baseline period compared to the upcoming period 2021–2040 under the selected scenarios were 1.43 and 1.65C, respectively. The increase in precipitation for the baseline period compared to the upcoming period (2021–2040) under the two scenarios were up to 2.93 and 1.95, respectively. Furthermore, longer return periods are accompanied by higher amounts of probable maximum precipitation under RCP4.5 and RCP8.5, where extreme precipitation is showing higher level of rise under the latter scenario.     

Spatial Modeling of Air Pollution in Studies of Its Short‐Term Health Effects

Summary In studies that estimate the short‐term effects of air pollution on health, daily measurements of pollution concentrations are often available from a number of monitoring locations within the study area. However, the health data are typically only available in the form of daily counts for the entire area, meaning that a corresponding single daily measure of pollution is required. The standard approach is to average the observed measurements at the monitoring locations, and use this in a log‐linear health model. However, as the pollution surface is spatially variable this simple summary is unlikely to be an accurate estimate of the average pollution concentration across the region, which may lead to bias in the resulting health effects. In this article, we propose an alternative approach that jointly models the pollution concentrations and their relationship with the health data using a Bayesian spatio‐temporal model. We compare this approach with the simple spatial average using a simulation study, by investigating the impact of spatial variation, monitor placement, and measurement error in the pollution data. An epidemiological study from Greater London is then presented, which estimates the relationship between respiratory mortality and four different pollutants.     

Improving phenological forecasting models for rape stem weevil,Ceutorhynchus napi Gyll., based on long‐term multisite datasets

New threshold‐based models to predict the start of invasion by the stem‐boring pest, the rape stem weevil (Ceutorhynchus napi Gyll.) of winter oilseed rape (Brassica napus L.), were developed and compared to published models using long‐term datasets on weather and weevil phenology from experimental locations in Germany and Luxembourg. Threshold values for daily records of maximum air temperature, mean soil temperature, sunshine duration and total precipitation were adjusted to local conditions on the date of first weevil migration in spring. Mean error and the root mean squared error were used to assess model quality, where the error is defined as the number of days between predicted and observed arrival of weevils on the crop (regardless of sign). Best model results predicted first crop invasion by rape stem weevil when the thresholds of daily maximum air temperature ≥7.8°C, mean soil temperature ≥6.6°C, daily total precipitation ≤1.0 mm and sunshine duration ≥1 h were matched. This model takes into account meteorological variables likely to influence conditions at the overwintering site of the weevils in the soil, as well as variables that may limit weevil flight. Adjusted air temperature threshold values were consistently lower for Luxembourg sites than for those optimized for Germany. A simple model relating the date of first weevil invasion to accumulated daily maximum air temperature above 0°C (from 1 January) was also evaluated. This proved less suitable for forecasting crop invasion by C. napi. We suggest that phenological models using locally adjusted meteorological‐based thresholds have the potential to offer sufficiently accurate forecasts of first immigration flights by C. napi for appropriate timing of insecticide application. In addition, the developed models are suitable tools to be used in climate change impact studies.