Toward integrated assessment of environmental change: air pollution health effects in the USA

We demonstrate a method for integrating environmental effects into a computable general equilibrium model. This is a critical step forward toward the development of improved integrated assessment models of environmental change. We apply the method to examine the economic consequences of air pollution on human health for the US for the period from 1970 to 2000. The pollutants include tropospheric ozone, nitrogen dioxide, sulfur dioxide, carbon monoxide, and particulate matter. We apply this method to the MIT Emissions Prediction and Policy Analysis (EPPA) model, a computable general equilibrium model of the economy that has been widely used to study climate change policy. The method makes use of traditional valuation studies, incorporating this information so that estimates of welfare change are consistent with welfare valuation of the consumption of market goods and services. We estimate the benefits of air pollution regulations in USA rose steadily from 1975 to 2000 from $50 billion to $400 billion (from 2.1% to 7.6% of market consumption). Our estimated benefits of regulation are somewhat lower than the original estimates made by the US Environmental Protection Agency, and we trace that result to our development of a stock model of pollutant exposure that predicts that the benefits from reduced chronic air pollution exposure will only be gradually realized. We also estimate the economic burden of uncontrolled levels of air pollution over that period. The uncertainties in these estimates are large which we show through simulations using 95% confidence limits on the epidemiological dose-response relationships     

Estimating land-surface temperature under clouds using MSG/SEVIRI observations

The retrieval of land-surface temperature (LST) from thermal infrared satellite sensor observations is known to suffer from cloud contamination. Hence few studies focus on LST retrieval under cloudy conditions. In this paper a temporal neighboring-pixel approach is presented that reconstructs the diurnal cycle of LST by exploiting the temporal domain offered by geo-stationary satellite observations (i.e. MSG/SEVIRI), and yields LST estimates even for overcast moments when satellite sensor can only record cloud-top temperatures. Contrasting to the neighboring pixel approach as presented by Jin and Dickinson (2002), our approach naturally satisfies all sorts of spatial homogeneity assumptions and is hence more suited for earth surfaces characterized by scattered land-use practices. Validation is performed against in situ measurements of infrared land-surface temperature obtained at two validation sites in Africa. Results vary and show a bias of −3.68 K and a RMSE of 5.55 K for the validation site in Kenya, while results obtained over the site in Burkina Faso are more encouraging with a bias of 0.37 K and RMSE of 5.11 K. Error analysis reveals that uncertainty of the estimation of cloudy sky LST is attributed to errors in estimation of the underlying clear sky LST, all-sky global radiation, and inaccuracies inherent to the ‘neighboring pixel’ scheme itself. An error propagation model applied for the proposed temporal neighboring-pixel approach reveals that the absolute error of the obtained cloudy sky LST is less than 1.5 K in the best case scenario, and the uncertainty increases linearly with the absolute error of clear sky LST. Despite this uncertainty, the proposed method is practical for retrieving the LST under a cloudy sky condition, and it is promising to reconstruct diurnal LST cycles from geo-stationary satellite observations.     

Mapping grassland leaf area index with airborne hyperspectral imagery: A comparison study of statistical approaches and inversion of radiative transfer models

Statistical and physical models have seldom been compared in studying grasslands. In this paper, both modeling approaches are investigated for mapping leaf area index (LAI) in a Mediterranean grassland (Majella National Park, Italy) using HyMap airborne hyperspectral images. We compared inversion of the PROSAIL radiative transfer model with narrow band vegetation indices (NDVI-like and SAVI2-like) and partial least squares regression (PLS). To assess the performance of the investigated models, the normalized RMSE (nRMSE) and R² between in situ measurements of leaf area index and estimated parameter values are reported. The results of the study demonstrate that LAI can be estimated through PROSAIL inversion with accuracies comparable to those of statistical approaches (R² = 0.89, nRMSE = 0.22). The accuracy of the radiative transfer model inversion was further increased by using only a spectral subset of the data (R² = 0.91, nRMSE = 0.18). For the feature selection wavebands not well simulated by PROSAIL were sequentially discarded until all bands fulfilled the imposed accuracy requirements.     

Interannual variability of NDVI and bird species diversity in Kenya

Species richness, or simply the number of species in a given area, is commonly used as an important indicator of biological diversity. Spatial variability in species richness has been postulated to depend upon environmental factors such as climate and climatic variability, which in turn may affect net primary productivity. The Advanced Very High Resolution Radiometer (AVHRR) derived Normalized Difference Vegetation Index (NDVI) has been shown to be correlated with climatic variables including rainfall, actual evapotranspiration and net primary productivity. To determine factors favoring high species richness, we examined the relationship between interannual NDVI variables and species richness of birds at a quarter degree scale (55 × 55 km). Results revealed a strong positive correlation between species richness and maximum average NDVI. Conversely, species richness showed negative correlation with standard deviation of maximum NDVI and the coefficient of variation. Though these relationships are indirect, they apparently operate through the green vegetation cover. Understanding such relationships can help in mapping and monitoring biological diversity, as well as in estimating changes in species richness in response to global climatic change.     

Estimation of green grass/herb biomass from airborne hyperspectral imagery using spectral indices and partial least squares regression

The main objective was to determine whether partial least squares (PLS) regression improves grass/herb biomass estimation when compared with hyperspectral indices, that is normalised difference vegetation index (NDVI) and red-edge position (REP). To achieve this objective, fresh green grass/herb biomass and airborne images (HyMap) were collected in the Majella National Park, Italy in the summer of 2005. The predictive performances of hyperspectral indices and PLS regression models were then determined and compared using calibration (n = 30) and test (n = 12) data sets. The regression model derived from NDVI computed from bands at 740 and 771 nm produced a lower standard error of prediction (SEP = 264 g m⁻²) on the test data compared with the standard NDVI involving bands at 665 and 801 nm (SEP = 331 g m⁻²), but comparable results with REPs determined by various methods (SEP = 261 to 295 g m⁻²). PLS regression models based on original, derivative and continuum-removed spectra produced lower prediction errors (SEP = 149 to 256 g m⁻²) compared with NDVI and REP models. The lowest prediction error (SEP = 149 g m⁻², 19% of mean) was obtained with PLS regression involving continuum-removed bands. In conclusion, PLS regression based on airborne hyperspectral imagery provides a better alternative to univariate regression involving hyperspectral indices for grass/herb biomass estimation in the Majella National Park.     

Assessing trends and seasonal changes in elephant poaching risk at the small area level using spatio-temporal Bayesian modeling

Knowledge about changes in wildlife poaching risk at fine spatial scale can provide essential background intelligence for law enforcement and crime prevention. We assessed interannual trends and seasonal changes in elephant poaching risk for Kenya’s Greater Tsavo ecosystem for 2002 to 2012 using spatio-temporal Bayesian modeling. Poaching data were obtained from the Kenya Wildlife Service’s database on elephant mortality. The novelty of our paper is (1) combining space and time when defining poaching risk for elephant; (2) the inclusion of environmental risk factors to improve the accuracy of the spatio-temporal Bayesian model; and (3) the separate analysis of dry and wet seasons to understand season-dependent poaching patterns. Although Tsavo’s overall poaching level increased over time, the risk of poaching differed significantly across space. Three of the 34 spatial units had a consistently high poaching risk regardless of whether models included environmental risk factors. Adding risk factors enhanced the model’s predictive power. We found that highest poaching risk areas differed between the wet and dry season. The findings improve our understanding of elephant poaching and highlight high risk areas within Tsavo where action to reduce elephant poaching is required.     

LAI and chlorophyll estimation for a heterogeneous grassland using hyperspectral measurements

The study shows that leaf area index (LAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) can be mapped in a heterogeneous Mediterranean grassland from canopy spectral reflectance measurements. Canopy spectral measurements were made in the field using a GER 3700 spectroradiometer, along with concomitant in situ measurements of LAI and LCC. We tested the utility of univariate techniques involving narrow band vegetation indices and the red edge inflection point, as well as multivariate calibration techniques, including stepwise multiple linear regression and partial least squares regression. Among the various investigated models, CCC was estimated with the highest accuracy (, ). All methods failed to estimate LCC (), while LAI was estimated with intermediate accuracy ( values ranged from 0.49 to 0.69). Compared with narrow band indices and red edge inflection point, stepwise multiple linear regression generally improved the estimation of LAI. The estimations were further improved when partial least squares regression was used. When a subset of wavelengths was analyzed, it was found that partial least squares regression had reduced the error in the retrieved parameters. The results of the study highlight the significance of multivariate techniques, such as partial least squares regression, rather than univariate methods such as vegetation indices in estimating heterogeneous grass canopy characteristics.     

Oceanic thunderstorms

Summary The oceanic thunderstorm has long been a difficult phenomenon to investigate. The diurnal variation of these storms together with that of land based storms is essential in providing the statistical correlation between global thunderstorm activity and oceanic potential gradients upon which the classical explanation of the maintenance of the earth's electric charge is based. A data base of over seven million synoptic observations by ships throughout the world ocean has been analyzed to give a fresh approach and insight to the universal time variation of ocean areas affected by thunderstorms. According to these data, the annual average of this ocean area has a mean of 202×10⁴ km² with a diurnal peak of 240×10⁴ km² in the interval of 1800 to 2400 GMT. A diurnal minimum is observed to be 167×10⁴ km² in the interval of 0600 to 1200 GMT.

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Zusammenfassung Es war lange Zeit schwierig, die ozeanischen Gewitter zu untersuchen. Die klassische Erklärung für die Aufrechterhaltung der elektrischen Erdladung gründet sich auf den Zusammenhang zwischen dem luftelektrischen Potentialgefälle auf See und der weltweiten Gewittertätigkeit, wofür die Kenntnis der Tagesgänge der Gewitter über Land und über den Ozean eine wichtige Voraussetzung bilden. Beobachtungsmaterial mit mehr als sieben Millionen synoptischen Beobachtungen — gewonen von Schiffen auf allen Ozeanen — wurde ausgewertet um auf eine neue Weise die von Gewittern bedeckten ozeanischen Flächengrösse in ihrer nach Weltzeit verlaufenden Variation zu untersuchen. Es stellt sich heraus dass das Jahresmittel dieser Flächengrösse bei 202·10⁴ km² liegt, mit einem tageszeitlichen Höchstwert von 240·10⁴ km² in der Zeit zwischen 1800 und 2400 GMT. Ein tageszeitlicher Mindestwert wurde zu 167·10⁴ km² gefunden, welcher zwischen 0600 und 1200 GMT auftritt.