Revealing causality in the associations between meteorological variables and air pollutant concentrations.

Understanding the role of meteorology in determining air pollutant concentrations is an important goal for better comprehension of air pollution dispersion and fate. It requires estimating the strength of the causal associations between all the relevant meteorological variables and the pollutant concentrations. Unfortunately, many of the meteorological variables are not routinely observed. Furthermore, the common analysis methods cannot establish causality. Here we use the output of a numerical weather prediction model as a proxy for real meteorological data, and study the causal relationships between a large suite of its meteorological variables, including some rarely observed ones, and the corresponding nitrogen dioxide (NO2) concentrations at multiple observation locations. Time-lagged convergent cross mapping analysis is used to ascertain causality and its strength, and the Pearson and Spearman correlations are used to study the direction of the associations. The solar radiation, temperature lapse rate, boundary layer height, horizontal wind speed and wind shear were found to be causally associated with the NO2 concentrations, with mean time lags of their maximal impact at -3, -1, -2 and -3 hours, respectively. The nature of the association with the vertical wind speed was found to be uncertain and region-dependent. No causal association was found with relative humidity, temperature and precipitation.

Global rainfall erosivity database (GloREDa) and monthly R-factor data at 1 km spatial resolution.

Here, we present and release the Global Rainfall Erosivity Database (GloREDa), a multi-source platform containing rainfall erosivity values for almost 4000 stations globally. The database was compiled through a global collaboration between a network of researchers, meteorological services and environmental organisations from 65 countries. GloREDa is the first open access database of rainfall erosivity (R-factor) based on hourly and sub-hourly rainfall records at a global scale. This database is now stored and accessible for download in the long-term European Soil Data Centre (ESDAC) repository of the European Commission's Joint Research Centre. This will ensure the further development of the database with insertions of new records, maintenance of the data and provision of a helpdesk. In addition to the annual erosivity data, this release also includes the mean monthly erosivity data for 94% of the GloREDa stations. Based on these mean monthly R-factor values, we predict the global monthly erosivity datasets at 1 km resolution using the ensemble machine learning approach (ML) as implemented in the mlr package for R. The produced monthly raster data (GeoTIFF format) may be useful for soil erosion prediction modelling, sediment distribution analysis, climate change predictions, flood, and natural disaster assessments and can be valuable inputs for Land and Earth Systems modelling.

Low and High Ambient Temperatures during Pregnancy and Birth Weight among 624,940 Singleton Term Births in Israel (2010-2014): An Investigation of Potential Windows of Susceptibility.

BACKGROUND: Exposure to heat during pregnancy has been associated with reduced fetal growth. Less is known about associations with cold and the potential for critical time windows of exposure. OBJECTIVES: We aimed to evaluate, in a national retrospective cohort, critical windows of susceptibility during pregnancy to extreme temperatures (low and high) and fetal growth, among 624,940 singleton term births in Israel during the period 2010-2014. METHODS: Temperature exposures were estimated using a spatially refined gridded climate data set with a 1-h and 1-km2 resolution. Percentiles of temperature were categorized by climatic zone for the entire pregnancy and by trimesters and weeks. Generalized additive models with the distributed lag nonlinear model framework were used to estimate unadjusted and adjusted associations between percentiles and categories of temperature and fetal growth markers: term [births after 36 weeks of gestational age (GA)] mean birth weight and term low birth weight (tLBW, term infants with birth weight below 2,500g). RESULTS: After adjustment, extreme temperatures (percentiles) during the entire pregnancy were associated with a lower mean birth weight {≤10th vs. 41st-50th percentile: -56g [95% confidence interval (CI): -63g, -50g)]; >90th vs. 41st-50th percentile: -65g; 95% CI: -72g, -58g}. Similar inverse U-shaped patterns were observed for all trimesters, with stronger associations for heat than for cold and for exposures during the third trimester. For heat, results suggest critical windows between 3-9 and 19-34 GA-weeks, with the strongest association estimated at 3 GA-weeks (temperature >90th vs. 41st-50th percentiles: -3.8g; 95% CI: -7.1g, -0.4g). For cold, there was a consistent trend of null associations early in pregnancy and stronger inverse associations over time, with the strongest association at 36 GA-week (≤10th vs. 41st-50th percentiles: -2.9g; 95% CI: -6.5g, 0.7g). For tLBW, U-shape patterns were estimated for the entire pregnancy and third trimester exposures, as well as nonsignificant associations with heat for 29-36 GA-weeks. Generally, the patterns of associations with temperatures during the entire pregnancy were consistent when stratified by urbanicity and geocoding hierarchy, when estimated for daily minimum and maximum temperatures, when exposures were classified based on temperature distributions in 49 natural regions, and when estimated for all live births. DISCUSSION: Findings from our study of term live births in Israel (2010-2014) suggest that exposure to extreme temperatures, especially heat, during specific time windows may result in reduced fetal growth. https://doi.org/10.1289/EHP8117.

Emissions vs. turbulence and atmospheric stability: A study of their relative importance in determining air pollutant concentrations.

Air pollution in the urban environment is a major concern. The ambient concentrations depend on the levels of transboundary imported pollution, the intensity of local sources and the prevailing atmospheric conditions. This work studies the relative impact of two atmospheric variables-atmospheric stability and regional scale turbulence-in determining the air pollution concentrations. We considered a setting (downtown Haifa, Israel) impacted by a large variety of sources, emitting pollutants with different chemical attributes and atmospheric life times. We found that traffic accounts for most of the locally produced pollution in the study location. However, the meteorological factors can overwhelm its impact and dictate the concentrations. The switch from stable to convective conditions and the more vigorous daytime wind are associated with a premature end of the morning peak concentrations that result from rush hour emissions of NOx, Black Carbon (BC) and ultra-fine particles. It results in daytime concentration which are lower than (winter) or equal to (summer) those at night, in spite of the much lower night-time traffic volumes. Similar, albeit weaker, impact was detected in the benzene and toluene concentrations. Sources outside the study area are responsible for most of the CO, PM1 and PM2.5 concentrations but during winter nights, characterised by strong atmospheric stability and low turbulence, their concentrations are elevated due to the local emissions. We developed a diagnostic statistical nonlinear model for the pollutant concentrations, which points to a stronger association of the atmospheric stability with the concentrations during stable conditions but turbulence dominating during convective conditions. Our findings explain the relatively low overall concentrations of locally emitted pollutants in the study area but warn of the potential for high concentrations during night-time in locations with comparable meteorological conditions.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records.

The exposure of the Earth's surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha-1 h-1 yr-1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

Data storage in optical fibers and reconstruction by use of low-coherence spectral interferometry.

We demonstrate optical data storage in optical fibers and reconstruction by use of low-coherence spectral interferometry. The information was stored by means of writing fiber Bragg gratings with different central wavelengths at different locations of the fiber. We need only a single short pulse is needed to read all the stored data. The maximum theoretical reconstruction rate that can be obtained with our technique is 10 Tbits/s. Our storage technique can be useful for identifying users in optical communication networks.