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We conducted this research as our start-up's research that addresses the problem of biogas production in cow-dense regions like India. We hypothesized that the thermophilic temperature (45-60^oC) would increase biogas production. The production process is much faster and more abundant at temperatures around 55-60^oC.

Several studies have applied different machine learning (ML) techniques to the area of forecasting solar photovoltaic power production. Most of these studies use weather data as inputs to predict power production; however, there are numerous practical issues with the procurement of this data. This study proposes models that do not use weather data as inputs, but rather use past power production data as a more practical substitute to weather-based models. Our proposed models demonstrate a better, cheaper, and more reliable alternatives to current weather models.

About 25% of the food grown never reaches consumers due to spoilage, and 11.5 billion pounds of produce from gardens are wasted every year. Current solutions involve farmers manually looking for and treating diseased crops. These methods of tending crops are neither time-efficient nor feasible. I used a convolutional neural network to identify signs of plant disease on leaves for garden owners and farmers.

Coral bleaching is a fatal process that reduces coral diversity, leads to habitat loss for marine organisms, and is a symptom of climate change. This process occurs when corals expel their symbiotic dinoflagellates, algae that photosynthesize within coral tissue providing corals with glucose. Restoration efforts have attempted to repair damaged reefs; however, there are over 360,000 square miles of coral reefs worldwide, making it challenging to target conservation efforts. Thus, predicting the likelihood of bleaching in a certain region would make it easier to allocate resources for conservation efforts. We developed a machine learning model to predict global locations at risk for coral bleaching. Data obtained from the Biological and Chemical Oceanography Data Management Office consisted of various coral bleaching events and the parameters under which the bleaching occurred. Sea surface temperature, sea surface temperature anomalies, longitude, latitude, and coral depth below the surface were the features found to be most correlated to coral bleaching. Thirty-nine machine learning models were tested to determine which one most accurately used the parameters of interest to predict the percentage of corals that would be bleached. A random forest regressor model with an R-squared value of 0.25 and a root mean squared error value of 7.91 was determined to be the best model for predicting coral bleaching. In the end, the random model had a 96% accuracy in predicting the percentage of corals that would be bleached. This prediction system can make it easier for researchers and conservationists to identify coral bleaching hotspots and properly allocate resources to prevent or mitigate bleaching events.

With monitoring of climate change and the evolving properties of the atmosphere more critical than ever, the authors of this study take sea salt aerosols into consideration. These sea salt aerosols, sourced from the bubbles found at the surface of the sea, serve as cloud condensation nuclei (CCN) and are effective for the formation of clouds, light scattering in the atmosphere, and cooling of the climate. With amines being involved in the process of CCN formation, the authors explore the effects of alkylamines on the properties of sea salt aerosols and their potential relevance to climate change.

Concerns regarding the rapid spread of Sars-CoV2 in early 2020 led company and local governmental officials in many states to ask people to work from home and avoid leaving their homes; measures commonly referred to as shutdowns. Here, the authors investigate how shutdowns affected carbon monoxide (CO) levels in 15 US states using publicly available data. Their results suggest that CO levels decreased as a result of these measures over the course of 2020, a trend which started to reverse after shutdowns ended.

Engineered bacteria that degrade oil are currently being considered as a safe option for the treatment of oil spills. For this approach to be successful, the bacteria must effectively express oil-degrading genes they uptake as part of an external genoming vehicle called a "plasmid". Using a computational approach, the authors investigate plasmid-bacterium compatibility to find pairs that ensure high levels of gene expression.

Clean water is a necessity for every household, yet water pollution is a serious problem in many parts of the world and plays a major role in compromising water security in the 21st century. In this paper, the authors address the utility of several plants as natural water purifiers. They estimate the effectiveness of duckweed, hyacinth, and azolla in improving the quality of water from the Mithi river in India by measuring several metrics. They conclude that all three plants are effective in improving water quality, suggesting that these plants as eco-friendly options for water treatment.

Algal overgrowth often threatens to clog irrigation pipes and drinking water lines when left unchecked, as well as releasing possible toxins that threaten plant and human health. It is thus important to find natural, non-harmful agents that can decrease algal growth without threatening the health of plants and humans. In this paper, the authors test the efficacy of barely extract in either liquid or pellet form in decreasing algal growth. While their results were inconclusive, the experimental set-up allows them to investigate a wider range of agents as anti-algal treatments that could potentially be adopted on a wider scale.

In this article, the authors compare different resource-efficient farming methods for the vegetable Lactuca sativa. They compared hydroponics (solid growth medium with added nutrients) to aquaponics (water with fish waste to provide nutrients) and determined efficacy by measuring plant height over time. While both systems supported plant growth, the authors concluded that aquaponics was the superior method for supporting Lactuca sativa growth. These findings are of great relevance as we continue to find the most sustainable and efficient means for farming.