Dynamic life cycle assessment for water treatment implications.

Long-term impact tracking of urban water services is an important scientific basis for the sustainable development goals of future foreground systems. This study developed a dynamic life cycle assessment (DLCA) method that considers temporal variation and the resulting impacts to address the challenges of water treatment facilities based on the principles of life cycle assessment (LCA) and system dynamics (SD) models. The model was then demonstrated and validated for a water treatment facility in the Kinmen Islands, Taiwan. The SD model simulates long-term water demand in terms of growth in the domestic, agriculture, livestock, and manufacturing sectors, which provides specific inventory data for LCA calculations, with the aim of showing the impact change for future water treatment scenarios. The results showed that using imported water and reclaimed water reduced Kinmen's reliance on groundwater from 77 % to 43 % and reduced the vulnerability of urban water services. The environmental impact of water treatment plants is determined to be strongly related to the efficiency of water treatment. In the long run, wastewater treatment plants can reduce their impacts with an increase in efficiency (3.7 % impact reduction). Additionally, the development of reclaimed water technology and water savings can reduce the impact by 19 % and 13.7 %, respectively, compared to the implementation of desalination. In terms of energy policy, more profound energy savings were observed when energy saving and structure transformation were simultaneously carried out. On the other hand, desalination poses the most political risk and has energy-associated environmental impacts. The DLCA results from this study showcase the trend of impact variation over time and thus provide valuable insights for future policy-making in mapping out the benefits and priorities of policy promotion.

Spatiotemporal impact of COVID-19 on Taiwan air quality in the absence of a lockdown: Influence of urban public transportation use and meteorological conditions.

The unprecedented outbreak of COVID-19 significantly improved the atmospheric environment for lockdown-imposed regions; however, scant evidence exists on its impacts on regions without lockdown. A novel research framework is proposed to evaluate the long-term monthly spatiotemporal impact of COVID-19 on Taiwan air quality through different statistical analyses, including geostatistical analysis, change detection analysis and identification of nonattainment pollutant occurrence between the average mean air pollutant concentrations from 2018-2019 and 2020, considering both meteorological and public transportation impacts. Contrary to lockdown-imposed regions, insignificant or worsened air quality conditions were observed at the beginning of COVID-19, but a delayed improvement occurred after April in Taiwan. The annual mean concentrations of PM10, PM2.5, SO2, NO2, CO and O3 in 2020 were reduced by 24%, 18%, 15%, 9.6%, 7.4% and 1.3%, respectively (relative to 2018-2019), and the overall occurrence frequency of nonattainment air pollutants declined by over 30%. Backward stepwise regression models for each air pollutant were successfully constructed utilizing 12 meteorological parameters (R2 > 0.8 except for SO2) to simulate the meteorological normalized business-as-usual concentration. The hybrid single-particle Lagrangian integrated trajectory (HYSPLIT) model simulated the fate of air pollutants (e.g., local emissions or transboundary pollution) for anomalous months. The changes in different public transportation usage volumes (e.g., roadway, railway, air, and waterway) moderately reduced air pollution, particularly CO and NO2. Reduced public transportation use had a more significant impact than meteorology on air quality improvement in Taiwan, highlighting the importance of proper public transportation management for air pollution control and paving a new path for sustainable air quality management even in the absence of a lockdown.