Environmental Research Communications
EISSN : 2515-7620
Published by: IOP Publishing (10.1088)
Total articles ≅ 295
Latest articles in this journal
Environmental Research Communications; https://doi.org/10.1088/2515-7620/ac2fbb
Environmental Research Communications; https://doi.org/10.1088/2515-7620/ac2f92
Estimates of daily air pollution concentrations with complete spatial and temporal coverage are important for supporting epidemiologic studies and health impact assessments. While numerous approaches have been developed for modeling air pollution, they typically only consider each pollutant separately. We describe a spatial multipollutant data fusion model that combines monitoring measurements and chemical transport model simulations that leverages dependence between pollutants to improve spatial prediction. For the contiguous United States, we created a data product of daily concentration for 12 pollutants (CO, NOx, NO2, SO2, O3, PM10, and PM2.5 species EC, OC, NO3, NH4, SO4) during the period 2005 to 2014. Out-of-sample prediction showed good performance, particularly for daily PM2.5 species EC (R2 = 0.64), OC (R2 = 0.75), NH4 (R2 = 0.84), NO3 (R2 = 0.73), and SO4 (R2 = 0.80). By employing the integrated nested Laplace approximation (INLA) for Bayesian inference, our approach also provides model-based prediction error estimates. The daily data product at 12km spatial resolution will be publicly available immediately upon publication. To our knowledge this is the first publicly available data product for major PM2.5 species and several gases at this spatial and temporal resolution.
Environmental Research Communications; https://doi.org/10.1088/2515-7620/ac2e6e
Local projections of future sea-level change are important for understanding climate change risks and informing coastal management decisions. Reliable and relevant coastal risk information is especially important in South Asia, where large populations live in low-lying areas and are at risk from coastal inundation. We present a new set of local sea-level projections for selected tide gauge locations in South Asia. The projections are used to explore the drivers of spatial variations in sea-level change for South Asia over the 21st century under the RCP2.6 and RCP8.5 scenarios. Global sea level rise for 2081-2100 is projected to be 0.39 m (0.26-0.58 m) and 0.65 m (0.47 m-0.93m) for RCP2.6 and RCP8.5 respectively. Local sea-level rise projections for the same period vary spatially over the South Asia region with local sea-level rise in excess of projected global sea level rise in equatorial Indian Ocean but less than projected global sea level rise for northern Arabian Sea and Bay of Bengal. Local sea level rise for 2081-2100 is projected to be 0.44 m (0.29-0.67 m) and 0.72 m (0.51-1.06 m) at Gan II (Maldives) under RCP2.6 and RCP8.5 respectively, whereas for Diamond Harbour (West Bengal) the corresponding changes are 0.32 m (0.19-0.51 m) and 0.57 m (0.39-0.85m). We find that the sterodynamic contribution is generally the leading driver of change at any single location, with future groundwater extraction over the sub-continent landmass the main driver of spatial variations in sea-level across the region. The new localised projections quantify and enhance understanding of future sea-level rise in South Asia, with the potential to feed into decisions for coastal planning by local communities, government, and industry.
Environmental Research Communications; https://doi.org/10.1088/2515-7620/ac2e6f
While it is widely recognized that extreme fires have been increasing under warming and drying climate, knowledge regarding the magnitude and intensity of extreme fires is very limited. Moreover, fire emissions reported by existing emissions inventories show large discrepancies due to different approaches and parameters. In this study, we analyzed the fire intensity and emissions magnitude of the 2019–2020 Australian bushfires using fire observations from multiple satellites. The results show that the bushfires were extreme in both their number and intensity, which were higher by a factor of 25 and 19, respectively, compared to the past two-decade seasonal mean. The 2019-2020 bushfires burned a total of 112.3 Tg biomass and released 178.6 ± 13.6 Tg CO2 (carbon dioxide), 1.71 ± 1.28 Tg PM2.5 (particulate matter with a diameter < 2.5 μm), and 0.061 ± 0.04 Tg BC (black carbon) across eastern and southern Australia. The CO2 emissions are 35% of Australia's greenhouse emissions from all sectors combined in 2020. Furthermore, the extreme fires in the most severe day and hour released 10% and 1.4% of the entire seasonal emissions, respectively. Our findings provide quantitative information for investigating the impacts of smoke emissions on air quality, ecosystem, and climate.
Environmental Research Communications, Volume 3; https://doi.org/10.1088/2515-7620/ac23a8
Environmental Research Communications, Volume 3; https://doi.org/10.1088/2515-7620/ac25d0
Environmental Research Communications; https://doi.org/10.1088/2515-7620/ac2b7d
Atmospheric reactive nitrogen (N) deposition is an important driver of carbon (C) sequestration in forest ecosystems. Previous studies have focused on N-C interactions in various ecosystems; however, relatively little is known about the impact of N deposition on ecosystem C cycling during climate extremes such as droughts. With the occurrence and severity of droughts likely to be exacerbated by climate change, N deposition – drought interactions remain one of the key uncertainties in process-based models to date. This study aims to contribute to the understanding of N deposition-drought dynamics on gross primary production (GPP) in European forest ecosystems. To do so, different soil water availability indicators (Standardized Precipitation Evapotranspiration Index (SPEI), soil volumetric water) and GPP measurements from European FLUXNET forest sites were used to quantify the response of forest GPP to drought. The computed drought responses of the forest GPP to drought were linked to modelled N deposition estimates for varying edaphic, physiological, and climatic conditions. Our result showed a differential response of forest ecosystems to the drought indicators. Although all FLUXNET forest sites showed a coherent dependence of GPP on N deposition, no consistent or significant N deposition effect on the response of forest GPP to drought could be isolated. The mean response of forest GPP to drought could be predicted for forests with Pinus trees as dominant species (R2 = 0.85, RMSE = 8.1). After extracting the influence of the most prominent parameters (mean annual temperature and precipitation, forest age), however, the variability remained too large to significantly substantiate hypothesized N deposition effects. These results suggest that, while N deposition clearly affects forest productivity, N deposition is not a major nor consistent driver of forest productivity responses to drought in European forest ecosystems.
Environmental Research Communications, Volume 3; https://doi.org/10.1088/2515-7620/ac2ab2
With the increasing spread of the Covid-19 pandemic, restrictions on public life were strengthened across the world. Non-pharmaceutical interventions like stay-at-home orders, cancellations of events, work from home etc. are the first line of defence to combat the spread of highly transmittable infections like Covid-19. But these interventions create whole new situations that urban residents need to cope with, which often creates mental distress. Home gardens, due to their therapeutic benefits, can help individuals to relax and unwind, thus lowering mental distress. Hence, the present study attempts to investigate whether home gardens moderate the effects on mental distress from confinement at homes due to the enforcement of stay-at-home orders. Samples (N = 408) were collected through an online question survey with urban residents across different parts of India. Moderation analysis reported the significant effect of home gardens in lowering mental distress. Further analysis with time spent in home gardens revealed that with increasing time spent from less than 10 minutes to more than 2 hours, an individual score of stress and anxiety, and overall Depression, Anxiety and Stress Scale (DASS)-21 scores had significantly decreased. These findings illustrate the beneficial properties of nature-based solutions, home gardens in this case, in improving mental health, even during the difficult times of the Covid-19 pandemic. Our results suggest the necessity of scaling up these nature-based solutions in urban planning processes to make the residents healthy and resilient.
Environmental Research Communications, Volume 3; https://doi.org/10.1088/2515-7620/ac2a74
Improving irrigation water management and enhancing water productivity (WP) is required to address future water scarcity in the sub-Saharan region. Maximizing WP by exposing the crop to a certain level of water stress using deficit irrigation (DI) is considered a promising strategy. To adopt DI strategies, a shred of comprehensive evidence concerning DI for different crops is required. This review aims to provide adequate information about the effect of DI on WP. The result showed that DI considerably increased WP compared to full irrigation. Despite higher WP, the reduced yield was obtained in some of the studied DI practices compared to full irrigation. It was also found that yield reduction may be low compared to the benefits gained by diverting the saved water to irrigate extra arable land. Maize revealed the highest (2.7 kg m-3) and lowest (0.5 kg m-3) WP when irrigated at only the initial stage compared with being fully irrigated in all growth stages. Also, onion showed a decreasing WP with increased irrigation water from 60% crop water requirement (ETc) (1.8 kg m-3) to 100% ETc (1.3 kg m-3). Increasing water deficit from 100 to 30% ETc led to an increase of wheat WP by 72%. For tomato, the highest WP (7.0 kg m-3) was found at 70% ETc followed by 50% ETc (7.0 kg m-3) and 85% ETc (6.9 kg m-3), while 100% ETc showed the least WP (6.8 kg m-3). Teff showed the lowest WP (1.7 kg m-3) under optimal irrigation, while it was highest (3.0 kg m-3) under 75% ETc throughout the growing season. The regression analysis for WP increment and yield reduction versus saved water showed higher values, indicating that DI could be an option for WP increment and increasing overall yield by expanding irrigated area and applying the saved water in water-scarce regions.
Environmental Research Communications, Volume 3; https://doi.org/10.1088/2515-7620/ac2894
San Clemente Island (SCI), located in the Southern California Bight, is owned and operated by the U.S. Navy and is home to endemic species, including federally threatened or endangered plants and birds. The SCI ecosystem is influenced by the presence of warm season low-level clouds that shade, cool and, especially when in the form of fog, moisten the environment. We created a new cloud and fog satellite-derived albedo product for SCI at a higher resolution than previous datasets. The record spans 23 summers (1996 – 2018, May - Sep). The spatial resolution is ~1 km and the temporal resolution is half hourly (nominally 0600 to 1800 PST). Using Geostationary Operational Environmental Satellite (GOES)-WEST visible band measurements, it was discovered that small (typically on the order of less than 5 km) geographical misalignment of the satellite images was common. The biological ramifications of such a shift could be significant. Thus, to provide a useful 1 km product for a narrow island such as SCI it was necessary to correct misalignments. Misalignment of albedo was easily apparent in clear sky images at the interface of land and water. This concept was used in the automated correction. The northwest coast of SCI is the cloudiest/foggiest area. June, on average, is the cloudiest month on SCI. The intra-day variability reaches ~20% cloud albedo while interannual and monthly variability are ~10%. To demonstrate the records' utility in understanding ecological phenomena and patterns, we used the dataset to model plant distributions. We found monthly mean cloud albedo values, a proxy for cloud frequency and persistence, were among the most important environmental variables in understanding plant distributions. The vegetation models indicate locations with appropriate conditions, including clouds and fog during critical periods of the year, for particular vegetation types and thus, can inform restoration and management activities.