The UN Sustainable Development Goal, SDG 7.3, is to double the global rate of improvement in energy efficiency by 2030. To meet this and other energy targets, countries encourage the development and adoption of energy-efficient products. An extensively researched phenomenon in this context is the energy rebound effect, especially in transportation. However, the direct relationship between the energy rebound effect and car emission levels has barely been investigated. Understanding this relationship is important, because energy-related emissions are closely linked to mortality, morbidity, and climate change. We assess the emission consequences in the private car market in Israel of a rebound effect associated with a policy promoting energy-efficient cars. We find that the baseline rebound before introduction of the policy was 40%. In the following three periods marked by policy changes, it grew to 54%, 69%, and 88%. Using household data with specific car characteristics and usage, we calculate the added greenhouse gas (GHG) emission consequences of this rebound by the end of the studied period to be about 5% of the country's per-capita target. Notably, estimates for the emission consequences using “average car” values were almost twice as high. The reason for this gap derives from the co-dependance between car usage and car efficiency. We discuss the implications of this gap in meeting emission goals.

Groundwater contamination originating from anthropogenic industrial activities is a global concern, adversely impacting health of living organisms and affecting natural ecosystems. Monitoring contamination in a complex groundwater system is often limited by sparse data and poor hydrogeological delineation, so that numerous indicators (organic, inorganic, isotopic) are frequently used simultaneously to reduce uncertainty. We suggest that selected Technology-Critical Elements (TCEs), which are usually found in very low concentrations in the groundwater environment, might serve as contamination indicators that can be monitored through aquifer systems. Here, we demonstrate the use of selected TCEs (in particular, Y, Rh, Tl, Ga, and Ge) as indicators for monitoring anthropogenic groundwater contamination in two different groundwater systems, near the Dead Sea, Israel. Using these TCEs, we show that the sources of local groundwater contamination are phosphogypsum ponds located adjacent to fertilizer plants in two industrial areas. In addition, we monitored the spatial distribution of the contaminant plume to determine the extent of well and spring contamination in the region. Results show significant contamination of the groundwater beneath both fertilizer plants, leading to contamination of a series of wells and two natural springs. The water in these springs contains elevated concentrations of toxic metals; U and Tl levels, among others, are above the maximum concentration limits for drinking water.

This manuscript evaluates spatial and temporal variations of source contributions to ambient fine particulate matter (PM2.5) in Israeli, Jordanian, and Palestinian cities. Twenty-four hour integrated PM2.5 samples were collected every six days over a 1-year period (January to December 2007) in four cities in Israel (West Jerusalem, Eilat, Tel Aviv, and Haifa), four cities in Jordan (Amman, Aqaba, Rahma, and Zarka), and three cities in Palestine (Nablus, East Jerusalem, and Hebron). The PM2.5 samples were analyzed for major chemical components, including organic carbon and elemental carbon, ions, and metals, and the results were used in a positive matrix factorization (PMF) model to estimate source contributions to PM2.5 mass. Nine sources, including secondary sulfate, secondary nitrate, mobile, industrial lead sources, dust, construction dust, biomass burning, fuel oil combustion and sea salt, were identified across the sampling sites. Secondary sulfate was the dominant source, contributing 35% of the total PM2.5 mass, and it showed relatively homogeneous temporal trends of daily source contribution in the study area. Mobile sources were found to be the second greatest contributor to PM2.5 mass in the large metropolitan cities, such as Tel Aviv, Hebron, and West and East Jerusalem. Other sources (i.e. industrial lead sources, construction dust, and fuel oil combustion) were closely related to local emissions within individual cities. This study demonstrates how international cooperation can facilitate air pollution studies that address regional air pollution issues and the incremental differences across cities in a common airshed. It also provides a model to study air pollution in regions with limited air quality monitoring capacity that have persistent and emerging air quality problems, such as Africa, South Asia and Central America.

The source for Lead (Pb) pollution in soils from the heavily industrialized area located along the coast of the Eastern Mediterranean, Haifa Bay, Northern Israel, is studied using the lead isotopic composition. The uniqueness of the studied data set is that it includes samples of soils, road-wash, and storm-dust sampled for nearly three decades (1988–2017). Road-wash sediments are similar in both elemental and Pb isotopic composition to soils sampled in the same year (2010), indicating re-suspension of local soil, as its origin. Soils sampled during and before 1993 show no evidence for Pb contamination (bulk soil values), although Pb as an additive was already in use. Furthermore, soil overturns hinder the possibility to trace changes in the Pb isotopic composition with time in soils of the same location. Soils sampled from 1995–8 to 2013 were significantly dominated by Post-1992 Pb additive, pointing to Pb’s peak as an additive. Soils Pb and Zn Enrichment factors for most samples are below 5, and their anthropogenic source is likely common. Forest fire enriched Pb and Zn in the soil, and their Pb isotope compositions reflect this enrichment. Lead from the Hod Assaf recycling plant detected up to some 2.5 km away, and although not analyzed in the current study, dioxin-like compounds possibly accompanied Pb.

Along the arid Arava, southern Israel, acacia trees (Acacia raddiana and Acacia tortilis) are considered keystone species. Yet they are threatened by the ongoing aquifer depletion for agriculture, the conversion of natural land to agricultural land, seed infestation by bruchid beetles, and the reduction in precipitation level in the region. In the acacia dominated Evrona reserve (southern Arava), adding to these threats are recurrent oil spills from an underground pipeline. We report here a study of the effects of contaminated soils, from a recent (December 2014) and a much older (1975) oil spills.The effects of local petroleum oil-contaminated soils on germination and early growing stages of the two acacia species were studied by comparisons with uncontaminated (control) soils from the same sites. For both acacia species, germination was significantly reduced in the 2014 oil-contaminated soils, whereas delayed in the 1975 oil-contaminated soil. There was no significant effect of oil volatile compounds on seed germination. At 105 days post transplanting (DPT), height, leaf number, stem diameter, and root growth were significantly smaller in the oil-contaminated soils. While photosynthetic performance (quantum yield of photosystem II) did not differ considerably between treatments, reductions of chlorophylls content and protein content were found in seedlings growing in the contaminated soils. Significant increases in superoxide dismutase (SOD) and ascorbate peroxidase (APX) activities were found in roots of seedlings growing in oil-contaminated soils. These results demonstrate that seed germination and seedling growth of both acacia species were strongly restricted by oil contamination in soils, from both recent (2014) and a 40-year old (1975) oil spills.Such long-term effects of oil spills on local acacia seedlings could shift the structure of local acacia communities. These results should be taken into account by local authorities aiming to clean up and restore such polluted areas.

This study applies remote sensing technology to assess and examine the spatial and temporal Brightness Temperature (BT) profile in the city of Tel-Aviv, Israel over the last 30 years using Landsat imagery. The location of warmest and coldest zones are constant over the studied period. Distinct diurnal and temporal BT behavior divide the city into four different segments. As an example of future application, we applied mixed regression models with daily random slopes to correlate Landsat BT data with monitored air temperature (Tair) measurements using 14 images for 1989–2014. Our preliminary results show a good model performance with R² = 0.81. Furthermore, based on the model's results, we analyzed the spatial profile of Tair within the study domain for representative days.

Crude oil pollution is a global environmental concern since it persists in the environment longer than most conventional carbon sources. In December 2014, the hyper-arid Evrona Nature Reserve, Israel, experienced large-scale contamination when crude oil spilled. The overarching goal of the study was to investigate the possible changes, caused by an accidental crude oil spill, in the leaf reflectance and biochemical composition of four natural habitat desert shrubs. The specific objectives were (1) to monitor the biochemical properties of dominant shrub species in the polluted and control areas; (2) to study the long-term consequences of the contamination; (3) to provide information that will assist in planning rehabilitation actions; and (4) to explore the feasibility of vegetation indices (VIs), along with the machine learning (ML) technique, for detecting stressed shrubs based on the full spectral range. Four measurement campaigns were conducted in 2018 and 2019. Along with the various stress indicators, field spectral measurements were performed in the range of 350–2500 nm. A regression analysis to examine the relation of leaf reflectance to biochemical contents was carried out, to reveal the relevant wavelengths in which polluted and control plants differ. Vegetation indices applied in previous studies were found to be less sensitive for indirect detection of long-term oil contamination. A novel spectral index, based on indicative spectral bands, named the “normalized blue-green stress index” (NBGSI), was established. The NBGSI distinguished significantly between shrubs located in the polluted and in the control areas. The NBGSI showed a strong linear correlation with pheophytin a. Machine learning classification algorithms obtained high overall prediction accuracy in distinguishing between shrubs located in the oil-polluted and the control sites, indicating internal component differences. The findings of this study demonstrate the efficacy of indirect and non-destructive spectral tools for detecting and monitoring oil pollution stress in shrubs.

To evaluate BPA's potential risk to health, it is important to know human daily intake. This study describes a simple but effective method to estimate the levels of human BPA intake among four different populations based on urinary concentration data. Nationally, of the 30 countries examined, the top ten countries for adult intake were Italy, Sweden, Denmark, France, Cyprus, Australia, Israel, Ghana, Jamaica, and Belgium. When the urinary excretion sample size was large enough and over 1000, it was found that the national estimated BPA daily intakes in the child group among countries, showed a good linear relationship with those of their corresponding adult group. Except the infant group with limited data, the global estimated BPA daily intakes for children and pregnant women were 2 and 1.4 times that of the adult group. Although the national and global estimated BPA daily intakes were generally below the temporary tolerable daily intake (tTDI) recommended by the European Food Safety Authority (EFSA), but some normal individuals' daily intakes exceeded the tTDI.

Over the last decades, changes in dust storms characteristics have been observed in different parts of the world. The changing frequency of dust storms in the southeastern Mediterranean has led to growing concern regarding atmospheric PM10 levels. A classic time series additive model was used in order to describe and evaluate the changes in PM10 concentrations during dust storm days in different cities in Israel, which is located at the margins of the global dust belt. The analysis revealed variations in the number of dust events and PM10 concentrations during 2001–2015. A significant increase in PM10 concentrations was identified since 2009 in the arid city of Beer Sheva, southern Israel. Average PM10 concentrations during dust days before 2009 were 406, 312, and 364 μg m−3 (median 337, 269,302) for Beer Sheva, Rehovot (central Israel) and Modi'in (eastern Israel), respectively. After 2009 the average concentrations in these cities during dust storms were 536, 466, and 428 μg m−3 (median 382, 335, 338), respectively. Regression analysis revealed associations between PM10 variations and seasonality, wind speed, as well as relative humidity. The trends and periodicity are stronger in the southern part of Israel, where higher PM10 concentrations are found. Since 2009 dust events became more extreme with much higher daily and hourly levels. The findings demonstrate that in the arid area variations of dust storms can be quantified easier through PM10 levels over a relatively short time scale of several years.