Machine Learning in Environmental Research: Common Pitfalls and Best Practices

Machine learning (ML) is increasingly used in environmental research to process large data sets and decipher complex relationships between system variables. However, due to the lack of familiarity and methodological rigor, inadequate ML studies may lead to spurious conclusions. In this study, we synthesized literature analysis with our own experience and provided a tutorial-like compilation of common pitfalls along with best practice guidelines for environmental ML research. We identified more than 30 key items and provided evidence-based data analysis based on 148 highly cited research articles to exhibit the misconceptions of terminologies, proper sample size and feature size, data enrichment and feature selection, randomness assessment, data leakage management, data splitting, method selection and comparison, model optimization and evaluation, and model explainability and causality. By analyzing good examples on supervised learning and reference modeling paradigms, we hope to help researchers adopt more rigorous data preprocessing and model development standards for more accurate, robust, and practicable model uses in environmental research and applications

ChatGPT and Environmental Research

ChatGPT and Environmental Researc

Microbial Electrolytic Carbon Capture for Carbon Negative and Energy Positive Wastewater Treatment

Energy and carbon neutral wastewater management is a major goal for environmental sustainability, but current progress has only reduced emission rather than using wastewater for active CO₂ capture and utilization. We present here a new microbial electrolytic carbon capture (MECC) approach to potentially transform wastewater treatment to a carbon negative and energy positive process. Wastewater was used as an electrolyte for microbially assisted electrolytic production of H₂ and OH^– at the cathode and protons at the anode. The acidity dissolved silicate and liberated metal ions that balanced OH^–, producing metal hydroxide, which transformed CO₂ in situ into (bi)­carbonate. Results using both artificial and industrial wastewater show 80–93% of the CO₂ was recovered from both CO₂ derived from organic oxidation and additional CO₂ injected into the headspace, making the process carbon-negative. High rates and yields of H₂ were produced with 91–95% recovery efficiency, resulting in a net energy gain of 57–62 kJ/mol-CO₂ captured. The pH remained stable without buffer addition and no toxic chlorine-containing compounds were detected. The produced (bi)­carbonate alkalinity is valuable for wastewater treatment and long-term carbon storage in the ocean. Preliminary evaluation shows promising economic and environmental benefits for different industries

Microbial Metabolism and Community Structure in Response to Bioelectrochemically Enhanced Remediation of Petroleum Hydrocarbon-Contaminated Soil

This study demonstrates that electrodes in a bioelectrochemical system (BES) can potentially serve as a nonexhaustible electron acceptor for <i>in situ</i> bioremediation of hydrocarbon contaminated soil. The deployment of BES not only eliminates aeration or supplement of electron acceptors as in contemporary bioremediation but also significantly shortens the remediation period and produces sustainable electricity. More interestingly, the study reveals that microbial metabolism and community structure distinctively respond to the bioelectrochemically enhanced remediation. Tubular BESs with carbon cloth anode (CCA) or biochar anode (BCA) were inserted into raw water saturated soils containing petroleum hydrocarbons for enhancing <i>in situ</i> remediation. Results show that total petroleum hydrocarbon (TPH) removal rate almost doubled in soils close to the anode (63.5–78.7%) than that in the open circuit positive controls (37.6–43.4%) during a period of 64 days. The maximum current density from the BESs ranged from 73 to 86 mA/m². Comprehensive microbial and chemical characterizations and statistical analyses show that the residual TPH has a strongly positive correlation with hydrocarbon-degrading microorganisms (HDM) numbers, dehydrogenase activity, and lipase activity and a negative correlation with soil pH, conductivity, and catalase activity. Distinctive microbial communities were identified at the anode, in soil with electrodes, and soil without electrodes. Uncommon electrochemically active bacteria capable of hydrocarbon degradation such as <i>Comamonas testosteroni, Pseudomonas putida, and Ochrobactrum anthropi</i> were selectively enriched on the anode, while hydrocarbon oxidizing bacteria were dominant in soil samples. Results from genus or phylum level characterizations well agree with the data from cluster analysis. Data from this study suggests that a unique constitution of microbial communities may play a key role in BES enhancement of petroleum hydrocarbons biodegradation in soils

Deep Learning Optimization for Soft Sensing of Hard-to-Measure Wastewater Key Variables

Soft sensors can be an essential part of a digital twin to acquire critical wastewater information for operation optimization. Soft sensor predictions have been successfully applied in nitrogen compounds, but hard-to-measure variables such as biochemical oxygen demand (BOD) and total suspended solids (TSS) have been a major challenge partially due to difficulty in capturing complex nonlinearity and needed information acquisition. This study pinpointed the bottlenecks by developing advanced hyperparameter optimized (HPO) deep learning (DL) models and testing different groups of data. By comparing two DL algorithms [multilayer perceptron and deep belief network (DBN)] with three HPO methods (genetic algorithm, particle swarm optimization (PSO), and grey wolf optimization), we found that DBN-PSO showed performance superior to other hybrid methods for both CBOD5 and TSS predictions based on 11 years of operational data. While the hybrid models exhibit complex topography, better results can be achieved with a slow learning process and a combination of aggressive pre-training and smooth fine-tuning for CBOD5 and TSS, respectively. Additional precipitation data did not provide additional benefits, whereas metal concentration data helped further improve the prediction accuracy (testing error index: 1.9 mg CBOD5/L and 1.5 mg TSS/L), suggesting that more diverse data acquisition is valuable for a better soft-sensor practice

Microbial Diversity and Biogeochemical Cycling of Nitrogen and Sulfur in the Source Region of the Lancang River on the Tibetan Plateau

The Tibetan Plateau is known as the “third pole” on Earth, influencing regional and global climates and providing fresh water to billions of people. Climate change and anthropogenic activities are increasingly threatening vulnerable ecosystems therein, but impacts on biogeochemical cycling and the microbial community are largely unknown. Hence, we characterized the distribution of nitrogen and sulfur species and bacterial communities from river water, sediment, and surrounding soils in the hard-to-access source region of the Lancang River. The dominant bacterial genera across Lancang River water were quite consistent, whereas they varied largely in different surface soils. Temperature was found to be a vital driver of bacterial community distribution in river water, while NH4+-N and reduced inorganic sulfur were inferred as major environmental drivers in soils. Unclassified Nitrosomonadaceae and Nitrospira were prevalent in all habitats. Network analysis inferred Nitrospira as a potential keystone genus. nirS genes and nitrite reductase quantification reveal denitrification proceeded in a 100–120 cm soil layer. The abundance of adenosine phosphosulfate reductase increased from the 30 cm soil layer upward, indicating more active sulfate reduction. Overall, this study provides the first comprehensive characterizations of biogeochemical cycling of nitrogen and sulfur from different habitats in the source region of the Lancang River

Cell-Free CO₂ Valorization to C6 Pharmaceutical Precursors via a Novel Electro-Enzymatic Process

The healthcare industry emits significant amounts of CO2 and has an imperative need for decarbonization. This study demonstrated a new hybrid electro-enzymatic process that converts waste CO2 into high-value C6 pharmaceutical precursor compounds. A novel three-chamber electrolyzer equipped with a Cu-based gas diffusion electrode converted gaseous CO2 into ethanol at a high current density (40–60 mA/cm2), high selectivity (43–81 mol %), and production rate (368–428 mg/L/h). Purified ethanol from the electrolyzer was then sent to an enzymatic bioreactor where ADH and DERA enzymes upgraded ethanol into C6 statin precursor molecules at high yields (29–35%) via acetaldehyde. Competitive C6 lactol synthesis rates (4.7–5.7 mM/day) and titers (712–752 mg/L) were achieved, demonstrating the potential of the end-to-end process. The C6 lactol product can seamlessly be converted to statins, a class of lipid-lowering medication that is among the largest selling class of drugs in the world. This hybrid process provides a new pathway for CO2 valorization to high-value products and accelerates healthcare sector decarbonization

Ca-Based Layered Double Hydroxides for Environmentally Sustainable Carbon Capture

The process of carbon dioxide capture typically requires a large amount of energy for the separation of carbon dioxide from other gases, which has been a major barrier to the widespread deployment of carbon capture technologies. Innovation of carbon dioxide adsorbents is herein vital for the attainment of a sustainable carbon capture process. In this study, we investigated the electrified synthesis and rejuvenation of calcium-based layered double hydroxides (Ca-based LDHs) as solid adsorbents for CO2. We discovered that the particle morphology and phase purity of the LDHs, along with the presence of secondary phases, can be controlled by tuning the current density during electrodeposition on a porous carbon substrate. The change in phase composition during carbonation and calcination was investigated to unveil the effect of different intercalated anions on the surface basicity and thermal stability of Ca-based LDHs. By decoupling the adsorption of water and CO2, we showed that the adsorbed water largely promoted CO2 adsorption, most likely through a sequential dissolution and reaction pathway. A carbon capture capacity of 4.3 ± 0.5 mmol/g was measured at 30 °C and relative humidity of 40% using 10 vol % CO2 in nitrogen as the feed stream. After CO2 capture occurred, the thermal regeneration step was carried out by directly passing an electric current through the conductive carbon substrate, known as the Joule-heating effect. CO2 was found to start desorbing from the Ca-based LDHs at a temperature as low as 220 °C as opposed to the temperature above 700 °C required for calcium carbonate that forms as part of the Ca-looping capture process. Finally, we evaluated the cumulative energy demand and environmental impact of the LDH-based capture process using a life cycle assessment. We identified the most environmentally concerning step in the process and concluded that the postcombustion CO2 capture using LDH could be advantageous compared with existing technologies

Active H₂ Harvesting Prevents Methanogenesis in Microbial Electrolysis Cells

Undesired H₂ sinks, including methanogenesis, are a serious issue faced by microbial electrolysis cells (MECs) for high-rate H₂ production. Different from current top-down approaches to methanogenesis inhibition that showed limited success, this study found active harvesting can eliminate the source (H₂) from all H₂ consumption mechanisms via rapid H₂ extraction using a gas-permeable hydrophobic membrane and vacuum. Active harvesting completely prevented CH₄ production and led to H₂ yields (2.62–3.39 mol of H₂/mol of acetate) much higher than that of the control using traditional spontaneous release (0.79 mol of H₂/mol of acetate). In addition, existing CH₄ production in the control MEC was stopped once the switch to active H₂ harvesting was made. Active harvesting also increased current density by 36%, which increased operation efficiency and facilitated organic removal. Energy quantification shows the process was energy-positive, as the H₂ energy produced via active harvesting was 220 ± 10% of external energy consumption, and a high purity of H₂ can be obtained