Systematic review and meta-analysis of human health-related protein markers for realizing real-time wastewater-based epidemiology.

For effective implementation of the wastewater-based epidemiology (WBE) approach, real-time quantification of markers in wastewater is critical for data acquisition before data interpretation, dissemination, and decision-making. This can be achieved by using biosensor technology, but whether the quantification/detection limits of different types of biosensors comply with the concentration of WBE markers in wastewater is unclear. In the present study, we identified promising protein markers with relatively high concentrations in wastewater samples and analyzed biosensor technologies that are potentially available for real-time WBE. The concentrations of potential protein markers in stool and urine samples were obtained through systematic review and meta-analysis. We examined 231 peer-review papers to collect information regarding potential protein markers that can enable us to achieve real-time monitoring using biosensor technology. Fourteen markers in stool samples were identified at the ng/g level, presumably equivalent to ng/L of wastewater after dilution. Moreover, relatively high average concentrations of fecal inflammatory proteins were observed, e.g., fecal calprotectin, clusterin, and lactoferrin. Fecal calprotectin exhibited the highest average log concentration among the markers identified in stool samples with its mean value being 5.24 [95 % CI: 5.05, 5.42] ng/g. We identified 50 protein markers in urine samples at the ng/mL level. Uromodulin (4.48 [95 % CI: 4.20, 4.76] ng/mL) and plasmin (4.18 [95 % CI: 3.15, 5.21] ng/mL) had the top two highest log concentrations in urine samples. Furthermore, the quantification limit of some electrochemical- and optical-based biosensors was found to be around the femtogram/mL level, which is sufficiently low to detect protein markers in wastewater even after dilution in sewer pipes.

Rapid estimation of CO2 emissions from forest fire events using cloud-based computation of google earth engine.

One of the main sources of greenhouse gases is forest fire, with carbon dioxide as its main constituent. With increasing global surface temperatures, the probability of forest fire events also increases. A method that enables rapid quantification of emissions is even more necessary to estimate the environmental impact. This study introduces the application of the Google Earth Engine platform to monitor burned areas in forest fire events in Mount Arjuno, Indonesia, during the 2016-2019 period, using Landsat-8 and Sentinel-2 satellite imageries. The events particularly affected grassland and tropical forest areas, as well as a fraction of agricultural areas, with a total estimated emission of 2.5 × 103 tCO2/km2 burned area. Higher carbon dioxide emissions were also observed, consistent with the higher local surface temperature as well as the CO total column mixing ratio average retrieved from Sentinel-5 p Tropospheric Monitoring Instrument during the period of analysis.