Massive slurry overproduction and poor management have resulted in environmental issues in various EU regions, notably in Germany. Despite the fact that slurry is used as an organic fertilizer, the frequency with which it is produced considerably outnumbers the area of animal farms. Techniques like acidification and alkalization have been proven to reduce greenhouse gas emissions by more than 90%. We showed that one of these processes, alkalization, allows the development of nutrient-deficient (N & P-based) sustainable fertilizers from raw pig slurry, which can be utilized in large amounts on fields without having a significant environmental impact. We evaluated these two techniques on the growth of ryegrass and maize in semi-greenhouse conditions on a smaller scale. We are notable for being the first to use this alkalization technology in plants. Alkalization enhances maize but has unfavorable effects on ryegrass, which might be due to frequent cuttings, long-term fertilization effects, and slurry pH (11 & 12). In terms of reducing emissions while having no effect on plant development, alkalized and acidified slurry are preferable to mineral fertilizer, especially for maize, whereas the effect of alkalized pH on ryegrass can be studied further in the future to see if there are any additional growth responses with various pH ranges. To summarise, this strategy is suggested to farmers since it allows them to utilize these sustainable fertilizers in bigger volumes on the field without having any negative influence on the environment

The world is undergoing a transition to a more sustainable energy sector dominated by renewable sources of energy. Climate change will increase the unpredictability of the weather, which calls for an increased resiliency of the future energy systems. This paper proposes an innovative solution that consists of catching water from streams at high altitudes to fill storage containers and transport them down a mountain, converting the potential energy of water into electricity and storing it in the truck's battery. The energy stored in the electric truck can be sold to the grid or used by the truck to transport other goods. Results show that the levelised cost of the electricity truck hydropower (ETH) is 30-100 USD/MWh, which is cheap when compared with conventional hydropower 50-200 USD/MWh. The electricity generation world potential for the technology is estimated to be 1.2 PWh per year, which is equivalent to around 4% of the global energy consumption in 2019. Apart from being a low cost and impact electricity generation technology, electric truck hydropower can operate in combination with solar and wind resources and provide energy storage services to the grid.

Long-term variations in temperature, rainfall, precipitation, or wind patterns are referred to as "Climate Change." Natural catastrophes are expected to become more frequent and intense as a result of climate change, with negative social, economic, and environmental implications. The majority of climate change in the contemporary era is due to anthropogenic activities. On the other hand, any human-caused climate change will be layered on top of a background of natural climatic variations that occur over a wide range of space and time ranges. The majority of people are unaware that plastics are made from fossil fuels. Plastic consumption accounts for around 6% of world oil consumption and is anticipated to rise to 20% by 2050. As a result of the energy-intensive operations necessary to extract and distill oil, plastic manufacturing produces massive amounts of greenhouse gas (GHG) emissions. This study reviews how the COVID-19 pandemic has temporarily reduced GHG emissions while increasing demand for single-use plastics, adding to the burden on an already out-of-control global plastic waste catastrophe. In the absence of effective treatment, governments around the world have mandated lockdown measures, as well as residents voluntarily limiting non-essential trips and activities. By early April 2020, daily global CO2 emissions had fallen by –17 percent (–11 to –25 percent for 1) when compared to the mean 2019 levels, with changes in surface transportation accounting for slightly less than half of the decline. Individual countries' emissions declined by 26% on average when they were at their peak. The impact on 2020 yearly emissions is dependent on the length of confinement, with a low estimate of –4% (–2 to –7%) if pre-pandemic circumstances recover by mid-June and a high estimate of –7% (–3 to –13%) if some limitations stay in place globally until the end of 2020. The total global CO2 reduction from January to April 2020 is expected to be more than 1749 Mt CO2 (a 14.3% decrease), with transportation accounting for the majority (58%) of the reduction, next off coal power generation (29%), and industry (10%). The COVID-19 pandemic has raised demand for single-use plastics, putting to the pressure on a worldwide plastic waste crisis that is already unmanageable. This mismanaged plastic waste (MMPW) is subsequently released into the environment, with some of it ending up in the ocean. The MMPW generated by the pandemic will be 11 million tons, culminating in a global riverine discharge of 34,000 tons into the ocean. As of August 23^rd 2021, 193 countries had produced 8.4 (+/-1.4) million tons of pandemic-related plastic waste, with 25.9 (+/-3.8) thousand tons dumped into the ocean, amounting to 1.5 percent (+/-0.2%) of global total riverine plastic discharge. Because of India's record-breaking confirmed cases, MMPW generation and discharge are projected to be more skewed toward Asia. As a result, transportation was identified as the primary source of more than half of the emissions reductions during the epidemic. This strongly suggests that changing typical working patterns, such as reducing commuting to work, working from home, and conducting online meetings or site visits, can have a real impact on GHG emissions. A considerable amount of the outflow is medical waste, which increases the risk to the environment and human health, or even the COVID-19 virus being spread. This demonstrates how waste management necessitates structural modifications. This review will aid individuals in comprehending the updating of the GHG management policy and use of plastic and its environmental repercussions in the event of a pandemic such as COVID-19.

Missing data in time series is a frequent problem for the environmental sciences. This is a serious limitation for statistical analysis and therefore, imputation (the process of filling missing data) is a keystone task. Several imputation methods have been proposed and implemented in programming software, however, their efficiency is data-dependent. There is no universal imputation method best for all time series, but instead, each method suits the structure of particular groups of time series. ¿Which imputation method is best to fill a time series? the main problem is that the target time series (of interest for imputation) already contains missing data, so validation of methods cannot be performed directly on it. Instead it needs a full time series (no missing data) to simulate missing data, perform imputations and compare actual to imputed. However, the best imputation method for the full time series is not necessarily the best for the target time series. The Known Sub- Sequence Algorithm (KSSA) is a novel approach to solve this problem by validating imputation methods directly on target time series. It uses the information contained within sub-sequences between missing data gaps to produce an optimal decision about a best imputation method for any particular target time series, no matter the structure it has. This is done by means of a process of iterative bootstraping that randomly samples sub-sequences of the target time series in order to learn from them to find a best method form a set of candidates. This is a promising machine learning algorithm that will help environmental scientists and decision makers working with time series. KSSA will soon be implemented as the ‘kssa’ R-package in CRAN and is currently available in GitHub. 

Soil pollution by heavy metals is a risk for environment and public health. Phytoremediation could be an alternative to chemical-physical techniques^1-3.
Three microorganisms, Trichoderma harzianum, Saccharomyces cerevisiae and Wickerhamomyces anomalus, were exposed in vitro to eight heavy metals (Ni, Cd, Cu, V, Zn, As, Pb, Hg). T. harzianum has showed the best bioaccumulation ability for V, As, Cd, Hg, Pb⁴. Then, T. harzianum was selected for the subsequent greenhouse test. Arundo donax and mycorrhized Arundo donax with T. harzianum were exposed for seven months at two different doses (L1 and L2) of the eight heavy metals mixture, to assess whether the symbiotic association could improve the bioaccumulation ability of the superior green plant A. donax.
Heavy metals were determined with ICP-MS⁵. The mean bioaccumulation percentage values of A. donax for L1 and L2 were, respectively: Ni (31%, 26%); Cd (35%, 50%); Cu (30%, 35%); As (19%, 27%); Pb (18%, 14%); (42%, 45%); V (39%, 26%); Zn (23%, 9%). The values of mycorrhized A. donax with T. harzianum for L1 and L2 were, respectively: Ni (27%, 38%); Cd (44%, 42%); Cu (36%, 29%); As (17%, 23%); Pb (37%, 54%); Hg (44%, 60%); V (16%, 20%); Zn (14%, 7%). A. donax showed the highest BAF (bioaccumulation factor) for Cd (0.50) and Hg (0.45) after exposure to L2; mycorrhized A. donax with T. harzianum showed the highest BAF for Hg (0.60), Pb (0.54) and Cd (0.42) after exposure to L2. The values of the TF (translocation factor) of A. donax were not particularly high, but A. donax mycorrhized with T. harzianum showed high TF values for Cd (0.70) and As (0.56) after exposure to L2, and Zn (0.30) after exposure to L1.

Our results suggest a possible use of both A. donax and A. donax mycorrhized with T. harzianum for phytoremediation processes.