Practical large-scale spatio-temporal modeling of particulate matter concentrations

The last two decades have seen intense scientific and regulatory interest in the health effects of particulate matter (PM). Influential epidemiological studies that characterize chronic exposure of individuals rely on monitoring data that are sparse in space and time, so they often assign the same exposure to participants in large geographic areas and across time. We estimate monthly PM during 1988--2002 in a large spatial domain for use in studying health effects in the Nurses' Health Study. We develop a conceptually simple spatio-temporal model that uses a rich set of covariates. The model is used to estimate concentrations of $PM_{10}$ for the full time period and $PM_{2.5}$ for a subset of the period. For the earlier part of the period, 1988--1998, few $PM_{2.5}$ monitors were operating, so we develop a simple extension to the model that represents $PM_{2.5}$ conditionally on $PM_{10}$ model predictions. In the epidemiological analysis, model predictions of $PM_{10}$ are more strongly associated with health effects than when using simpler approaches to estimate exposure. Our modeling approach supports the application in estimating both fine-scale and large-scale spatial heterogeneity and capturing space--time interaction through the use of monthly-varying spatial surfaces. At the same time, the model is computationally feasible, implementable with standard software, and readily understandable to the scientific audience. Despite simplifying assumptions, the model has good predictive performance and uncertainty characterization.Comment: Published in at http://dx.doi.org/10.1214/08-AOAS204 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Traffic particles and occurrence of acute myocardial infarction: a case–control analysis

OBJECTIVES: We modelled exposure to traffic particles using a latent variable approach and investigated whether long-term exposure to traffic particles is associated with an increase in the occurrence of acute myocardial infarction (AMI) using data from a population-based coronary disease registry. METHODS: Cases of individually validated AMI were identified between 1995 and 2003 as part of the Worcester Heart Attack Study. Population controls were selected from Massachusetts, USA, resident lists. NO(2) and PM(2.5) filter absorbance were measured at 36 locations throughout the study area. The air pollution data were used to estimate exposure to traffic particles using a semiparametric latent variable regression model. Conditional logistic models were used to estimate the association between exposure to traffic particles and occurrence of AMI. RESULTS: Modelled exposure to traffic particles was highest near the city of Worcester. Cases of AMI were more exposed to traffic and traffic particles compared to controls. An interquartile range increase in modelled traffic particles was associated with a 10% (95% CI 4% to 16%) increase in the odds of AMI. Accounting for spatial dependence at the census tract, but not block group, scale substantially attenuated this association. CONCLUSIONS: These results provide some support for an association between long-term exposure to traffic particles and risk of AMI. The results were sensitive to the scale selected for the analysis of spatial dependence, an issue that requires further investigation. The latent variable model captured variation in exposure, although on a relatively large spatial scale

Spatio-temporal modeling of particulate air pollution in the conterminous United States using geographic and meteorological predictors

Background: Exposure to atmospheric particulate matter (PM) remains an important public health concern, although it remains difficult to quantify accurately across large geographic areas with sufficiently high spatial resolution. Recent epidemiologic analyses have demonstrated the importance of spatially- and temporally-resolved exposure estimates, which show larger PM-mediated health effects as compared to nearest monitor or county-specific ambient concentrations. Methods: We developed generalized additive mixed models that describe regional and small-scale spatial and temporal gradients (and corresponding uncertainties) in monthly mass concentrations of fine (PM2.5), inhalable (PM10), and coarse mode particle mass (PM2.5–10) for the conterminous United States (U.S.). These models expand our previously developed models for the Northeastern and Midwestern U.S. by virtue of their larger spatial domain, their inclusion of an additional 5 years of PM data to develop predictions through 2007, and their use of refined geographic covariates for population density and point-source PM emissions. Covariate selection and model validation were performed using 10-fold cross-validation (CV). Results: The PM2.5 models had high predictive accuracy (CV R2=0.77 for both 1988–1998 and 1999–2007). While model performance remained strong, the predictive ability of models for PM10 (CV R2=0.58 for both 1988–1998 and 1999–2007) and PM2.5–10 (CV R2=0.46 and 0.52 for 1988–1998 and 1999–2007, respectively) was somewhat lower. Regional variation was found in the effects of geographic and meteorological covariates. Models generally performed well in both urban and rural areas and across seasons, though predictive performance varied somewhat by region (CV R2=0.81, 0.81, 0.83, 0.72, 0.69, 0.50, and 0.60 for the Northeast, Midwest, Southeast, Southcentral, Southwest, Northwest, and Central Plains regions, respectively, for PM2.5 from 1999–2007). Conclusions: Our models provide estimates of monthly-average outdoor concentrations of PM2.5, PM10, and PM2.5–10 with high spatial resolution and low bias. Thus, these models are suitable for estimating chronic exposures of populations living in the conterminous U.S. from 1988 to 2007

Predicting Chronic Fine and Coarse Particulate Exposures Using Spatiotemporal Models for the Northeastern and Midwestern United States

Background: Chronic epidemiologic studies of particulate matter (PM) are limited by the lack of monitoring data, relying instead on citywide ambient concentrations to estimate exposures. This method ignores within-city spatial gradients and restricts studies to areas with nearby monitoring data. This lack of data is particularly restrictive for fine particles (PM with aerodynamic diameter < 2.5 μm; PM2.5) and coarse particles (PM with aerodynamic diameter 2.5–10 μm; PM10–2.5), for which monitoring is limited before 1999. To address these limitations, we developed spatiotemporal models to predict monthly outdoor PM2.5 and PM10–2.5 concentrations for the northeastern and midwestern United States. Methods: For PM2.5, we developed models for two periods: 1988–1998 and 1999–2002. Both models included smooth spatial and regression terms of geographic information system-based and meteorologic predictors. To compensate for sparse monitoring data, the pre-1999 model also included predicted PM10 (PM with aerodynamic diameter < 10 μm) and extinction coefficients (km−1). PM10–2.5 levels were estimated as the difference in monthly predicted PM10 and PM2.5, with predicted PM10 from our previously developed PM10 model. Results: Predictive performance for PM2.5 was strong (cross-validation R2 = 0.77 and 0.69 for post-1999 and pre-1999 PM2.5 models, respectively) with high precision (2.2 and 2.7 μg/m3, respectively). Models performed well irrespective of population density and season. Predictive performance for PM10–2.5 was weaker (cross-validation R2 = 0.39) with lower precision (5.5 μg/m3). PM10–2.5 levels exhibited greater local spatial variability than PM10 or PM2.5, suggesting that PM2.5 measurements at ambient monitoring sites are more representative for surrounding populations than for PM10 and especially PM10–2.5. Conclusions: We provide semiempirical models to predict spatially and temporally resolved long-term average outdoor concentrations of PM2.5 and PM10–2.5 for estimating exposures of populations living in the northeastern and midwestern United States

Predicting the current distribution of the chacoan peccary (catagonus wagneri) in the gran Chaco

The Chacoan peccary (Catagonus wagneri), or Tagua, an endemic species living in the Chaco eco¬region, is endangered by highly increasing deforestation rates across the region, particularly in the last decade. This situation highlights the need to better understand the current distribution of the species, as well as how environmental conditions affect habitat suitability. This study predicts the distribution of the Chacoan peccary and evaluates the current environmental conditions in the Chaco for this species. Using six environmental variables and 177 confirmed occurrence records (from 2000 to 2015) provided by researchers, we developed a Species Distribution Model (SDM) applying the Maxent algorithm. The final model was highly accurate and significant (p < 0.001; AUC 0.860 ± 0.0268; omission error 1.82 %; post¬hoc validation of omission error using independent presence¬only records 1.33 %), predicting that 46.24 % of the Chaco is suitable habitat for the Chacoan peccary, with the most important areas concentrated in the middle of Paraguay and northern Argentina. Land cover, isothermality and elevation were the variables that better explained the habitat suitability for the Chacoan peccary. Despite some portions of suitable areas occurring inside protected areas, the borders and the central portions of suitable areas have recently suffered from intensive deforestation and development, and most of the highly suitable areas for the species are not under protection. The results provide fundamental insights for the establishment of priority Chacoan peccary conservation areas within its rangeFil: Paschoaletto Micchi, Katia Maria. Universidade Do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz Esalq; Brasil. Conservation Breeding Specialist Group Brazilian network; BrasilFil: Silva Angelieri, Cintia Camila. Universidade Do Sao Paulo. Escola Superior de Agricultura Luiz de Queiroz Esalq; BrasilFil: Altrichter, Mariana. Prescott College; Estados UnidosFil: Desbiez, Arnaud. Royal Zoological Society of Scotland. Edimburgo; Reino Unido. Conservation Breeding Specialist Group Brazilian network; BrasilFil: Yanosky, Alberto. Asociación Guyra Paraguay. Asunción; ParaguayFil: Campos Krauer, Juan Manuel. Centro Chaqueño para la Conservación y la Investigación; ParaguayFil: Torres, Ricardo Jose. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas, Físicas y Naturales; ArgentinaFil: Camino, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Centro de Ecología Aplicada del Litoral. Universidad Nacional del Nordeste. Centro de Ecología Aplicada del Litoral; ArgentinaFil: Cabral, Hugo. Asociación Guyra Paraguay. Asunción; ParaguayFil: Cartés, José. Asociación Guyra Paraguay. Asunción; ParaguayFil: Cuellar, Rosa Leny. Fundación Kaa Iya; BoliviaFil: Gallegos, Marcelo. Secretaría de Ambiente de la Provincia de Salta. Programa Guardaparques; ArgentinaFil: Giordano, Anthony J.. No especifica;Fil: Decarre, Julieta. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Recursos Biológicos; ArgentinaFil: Maffei, Leonardo. Wildlife Conservation Society. Lima; PerúFil: Neris, Nora. Universidad Nacional de Asunción; ParaguayFil: Saldivar Bellassai, Silvia. Itaipu Binacional; ParaguayFil: Wallace, Robert. Wildlife Conservation Society. New York; Estados UnidosFil: Lizarraga, Leónidas. Delegación Regional Noroeste. Sistema de Información de Biodiversidad de la Administración de Parques Nacionales. Salta; ArgentinaFil: Thompson, Jeffrey. Universidad Nacional de Asunción; ParaguayFil: Velilla, Mariela. Universidad Nacional de Asunción; Paragua

Protected area targets post-2020

The ten-year Strategic Plan for Biodiversity, made up of 20 Aichi Biodiversity Targets, is coming to an end and it is therefore timely to assess their appropriateness so as to provide scientific support on the development of an improved post-2020 framework. Here we focus on Aichi Target 11, concerned with conserving protected areas and other effective area-based conservation measures by 2020. We identify four broad problems with Aichi Target 11 that have led to perverse outcomes and an inability for nations to account for true conservation progress. We propose a formulation for a target for site-based conservation beyond 2020 aimed at overcoming them: ‘The value of all key biodiversity areas and other sites of global significance for biodiversity is documented and retained through protected areas and other effective area-based conservation measures’

A Previously Uncharacterized, Nonphotosynthetic Member of the Chromatiaceae Is the Primary CO_2-Fixing Constituent in a Self-Regenerating Biocathode

Biocathode extracellular electron transfer (EET) may be exploited for biotechnology applications, including microbially mediated O_2 reduction in microbial fuel cells and microbial electrosynthesis. However, biocathode mechanistic studies needed to improve or engineer functionality have been limited to a few select species that form sparse, homogeneous biofilms characterized by little or no growth. Attempts to cultivate isolates from biocathode environmental enrichments often fail due to a lack of some advantage provided by life in a consortium, highlighting the need to study and understand biocathode consortia in situ. Here, we present metagenomic and metaproteomic characterization of a previously described biocathode biofilm (+310 mV versus a standard hydrogen electrode [SHE]) enriched from seawater, reducing O_2, and presumably fixing CO_2 for biomass generation. Metagenomics identified 16 distinct cluster genomes, 15 of which could be assigned at the family or genus level and whose abundance was roughly divided between Alpha- and Gammaproteobacteria. A total of 644 proteins were identified from shotgun metaproteomics and have been deposited in the the ProteomeXchange with identifier PXD001045. Cluster genomes were used to assign the taxonomic identities of 599 proteins, with Marinobacter, Chromatiaceae, and Labrenzia the most represented. RubisCO and phosphoribulokinase, along with 9 other Calvin-Benson-Bassham cycle proteins, were identified from Chromatiaceae. In addition, proteins similar to those predicted for iron oxidation pathways of known iron-oxidizing bacteria were observed for Chromatiaceae. These findings represent the first description of putative EET and CO_2 fixation mechanisms for a self-regenerating, self-sustaining multispecies biocathode, providing potential targets for functional engineering, as well as new insights into biocathode EET pathways using proteomics

A social-ecological approach to identify and quantify biodiversity tipping points in South America’s seasonal dry ecosystems

ropical dry forests and savannas harbour unique biodiversity and provide critical ES, yet they are under severe pressure globally. We need to improve our understanding of how and when this pressure provokes tipping points in biodiversity and the associated social-ecological systems. We propose an approach to investigate how drivers leading to natural vegetation decline trigger biodiversity tipping and illustrate it using the example of the Dry Diagonal in South America, an understudied deforestation frontier. The Dry Diagonal represents the largest continuous area of dry forests and savannas in South America, extending over three million km² across Argentina, Bolivia, Brazil, and Paraguay. Natural vegetation in the Dry Diagonal has been undergoing large-scale transformations for the past 30 years due to massive agricultural expansion and intensification. Many signs indicate that natural vegetation decline has reached critical levels. Major research gaps prevail, however, in our understanding of how these transformations affect the unique and rich biodiversity of the Dry Diagonal, and how this affects the ecological integrity and the provisioning of ES that are critical both for local livelihoods and commercial agriculture.Fil: Thonicke, Kirsten. Institute for Climate Impact Research ; AlemaniaFil: Langerwisch, Fanny. Institute for Climate Impact Research ; Alemania. Czech University of Life Sciences Prague; República ChecaFil: Baumann, Matthias. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Leitão, Pedro J.. Humboldt Universität zu Berlin; Alemania. Technische Universitat Carolo Wilhelmina Zu Braunschweig.; AlemaniaFil: Václavík, Tomáš. Helmholtz Centre for Environmental Research; Alemania. Palacký University Olomouc; República ChecaFil: Alencar, Anne. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; BrasilFil: Simões, Margareth. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); BrasilFil: Scheiter, Simon. Empresa Brasileira de Pesquisa Agropecuária (EMBRAPA); Brasil. Universidade Federal do Rio de Janeiro; BrasilFil: Langan, Liam. Senckenberg Biodiversity and Climate Research Centre; AlemaniaFil: Bustamante, Mercedes. Universidade do Brasília; BrasilFil: Gasparri, Nestor Ignacio. Universidad Nacional de Tucumán. Facultad de Ciencias Naturales e Instituto Miguel Lillo. Instituto de Ecología Regional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán; ArgentinaFil: Hirota, Marina. Universidade Federal de Santa Catarina; Brasil. Universidade Estadual de Campinas; BrasilFil: Börner, Jan. Universitat Bonn; AlemaniaFil: Rajao, Raoni. Universidade Federal de Minas Gerais; BrasilFil: Soares Filho, Britaldo. Universidade Federal de Minas Gerais; BrasilFil: Yanosky, Alberto. Consejo Nacional de Ciencia y Tecnología; ParaguayFil: Ochoa Quinteiro, José Manuel. Instituto de Investigación de Recursos Biológicos Alexander von Humboldt; ColombiaFil: Seghezzo, Lucas. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Salta. Instituto de Investigaciones en Energía no Convencional. Universidad Nacional de Salta. Facultad de Ciencias Exactas. Departamento de Física. Instituto de Investigaciones en Energía no Convencional; ArgentinaFil: Conti, Georgina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto Multidisciplinario de Biología Vegetal. Universidad Nacional de Córdoba. Facultad de Ciencias Exactas Físicas y Naturales. Instituto Multidisciplinario de Biología Vegetal; ArgentinaFil: de la Vega Leiner, Anne Cristina. Universität Greifswald; Alemani

Genome-wide Association Study of Susceptibility to Particulate Matter–Associated QT Prolongation

BACKGROUND: Ambient particulate matter (PM) air pollution exposure has been associated with increases in QT interval duration (QT). However, innate susceptibility to PM-associated QT prolongation has not been characterized. OBJECTIVE: To characterize genetic susceptibility to PM-associated QT prolongation in a multi-racial/ethnic, genome-wide association study (GWAS). METHODS: Using repeated electrocardiograms (1986–2004), longitudinal data on PM<10 μm in diameter (PM10), and generalized estimating equations methods adapted for low-prevalence exposure, we estimated approximately 2.5×106 SNP×PM10 interactions among nine Women’s Health Initiative clinical trials and Atherosclerosis Risk in Communities Study subpopulations (n=22,158), then combined subpopulation-specific results in a fixed-effects, inverse variance-weighted meta-analysis. RESULTS: A common variant (rs1619661; coded allele: T) significantly modified the QT-PM10 association (p=2.11×10−8). At PM10 concentrations >90th percentile, QT increased 7 ms across the CC and TT genotypes: 397 (95% confidence interval: 396, 399) to 404 (403, 404) ms. However, QT changed minimally across rs1619661 genotypes at lower PM10 concentrations. The rs1619661 variant is on chromosome 10, 132 kilobase (kb) downstream from CXCL12, which encodes a chemokine, stromal cell-derived factor 1, that is expressed in cardiomyocytes and decreases calcium influx across the L-type Ca2+ channel. CONCLUSIONS: The findings suggest that biologically plausible genetic factors may alter susceptibility to PM10-associated QT prolongation in populations protected by the U.S. Environmental Protection Agency’s National Ambient Air Quality Standards. Independent replication and functional characterization are necessary to validate our findings. https://doi.org/10.1289/EHP34