Fu SH, Gasparrini A, Rodriguez PS, Jha P (2018) Mortality attributable to hot and cold ambient temperatures in India: a nationally representative case-crossover study. PLoS Med 15(7): e1002619. https://doi.org/10.1371/journal.pmed.1002619
Why was this study done?
Very few studies from low- and middle- income countries (LMICs) have examined daily hot and cold temperature effects on cause-specific mortality.
- This is, to our knowledge, the first study to estimate cause-specific deaths attributable to daily hot and cold temperatures in India using nationally representative mortality data spanning a 13-year period.
What did the researchers do and find?
- We used a case-crossover method and distributed-lag nonlinear models (DLNM) to assess the nonlinear and delayed associations between temperature and mortality risk.
- We found substantial numbers of cause-specific deaths attributable to moderately cold temperature, which were approximately 12 times greater than deaths due to extremely cold temperature and 42 times greater than deaths due to extremely hot temperature.
- Our results also showed that moderately cold temperature was associated with the highest number of deaths from stroke at ages 30–69 years and from respiratory diseases at ages 70 years and above.
What do these findings mean?
- Public health authorities should consider the detrimental effects of moderately cold and extremely hot temperatures in their mitigation strategies, particularly as the absolute population totals in India exposed to moderately cold and extremely hot temperatures have risen by about 270 and 10 million, respectively, in the last three decades.
- To provide reliable national estimates of temperature–mortality associations in other LMICs, large-scale and nationally representative mortality data are needed.
Santra D, Mandal S, Santra A, Ghorai UK. Cost effective and wireless portable device for estimation of hexavalent Chromium, Fluoride and Iron in drinking water. Anal Chem. 2018 Oct 3. doi: 10.1021/acs.analchem.8b03337.
The quality of drinking water often remains unknown to the people because of the inadequacy of cost-effective testing systems that can be used in field. Major portable instruments for water quality analysis include Ion Selective Electrodes (ISE) or Colorimeters. These are low cost devices but in case of multiple analyte detection like hexavalent Cr, Fluoride (F-) and Iron (Fe) with single instrumentation, no portable systems are available till date as per the authors’ knowledge. In this paper, we demonstrate the working of a low cost (approximate price INR 1500 or US $ 20) portable colorimetric system that can be operated with android smartphones wirelessly to estimate the contamination levels of Cr(VI), F-, or Fe in drinking water. This system also generates absorption spectra by recording absorbance of the analyte using Light Dependent Resistor (LDR) sensor. An android application software named “Spectruino” is developed to calculate the concentration of the analytes. We strongly believe that this cost-effective portable system will be very useful to ensure the drinking water quality throughout the continent to improve human health.
“Now isn’t it a bit odd that the authors made absolutely no mention of the ocean cycles in the abstract? As our regular readers know, the ocean cycles run surprisingly synchronous with the fluctuations in global temperatures, i.e. the key factors here are the AMO and PDO.” click here
“the dramatic impacts of climate change felt on coastlines and people across the Pacific are still anecdotal” see discussion here
Michino Hisabayashi, John Rogan & Arthur Elmes. Quantifying shoreline change in Funafuti Atoll, Tuvalu using a time series of Quickbird, Worldview and Landsat data Journal GIScience & Remote Sensing Volume 55, 2018 Issue 3
Funafuti Atoll, Tuvalu is located in the southwestern Pacific Ocean, which has experienced some of the highest rates of global sea-level rise over the past 60 years. Atoll islands are low-lying accumulations of reef-derived sediment that provide the only habitable land in Tuvalu, and are considered vulnerable to the myriad possible impacts of climate change, especially sea-level rise. This study examines the shoreline change of twenty-eight islands in Funafuti Atoll between 2005 and 2015 using 0.65 m QuickBird, 0.46 m WorldView-2, and 0.31 m WorldView-3 imagery using an image segmentation and decision tree classification. Shoreline change estimates are compared to previous study that used a visual interpretation approach. The feasibility of estimating island area with Landsat-8 Operational Land Imager (OLI) data is explored using CLASlite software. Results indicate a 0.13% (0.35 ha) decrease in net island area over the study time period, with 13 islands decreasing in area and 15 islands increasing in area. Substantial decreases in island area occurred on the islands of Fuagea, Tefala and Vasafua, which coincides with the timing of Cyclone Pam in March, 2015. Comparison between the WorldView-2 shoreline maps and those created from Landstat-8 indicate that the estimates tend to be in higher agreement for islands that have an area > 0.5 ha, a compact shape, and no built structures. Ten islands had > 90% agreement, with percent disagreements ranging from 2.78 to 100%. The methods and results of this study speak to the potential of automated EoV shoreline monitoring through segmentation and classification tree approach, which would reduce down data processing and analysis time. With the growing constellation of high and medium spatial resolution satellite-based sensors and the development of semi or fully automated image processing technology, it is now possible to remotely assess the short and medium-term shoreline dynamics on dynamic atolls. Landsat estimates were reasonably matched to those derived from fine resolution imagery, with some caveats about island size and shape.
“What kind of people sit around for days knowing an animal is suffering an agonizingly slow death and do nothing but plan how to use that suffering animal to make money? Callous and self-absorbed people.” click here
Crabbe H, Fletcher T, Close R, Watts MJ, Ander EL, Smedley PL, Verlander NQ, Gregory M, Middleton DRS, Polya DA, Studden M, Leonardi GS. Hazard Ranking Method for Populations Exposed to Arsenic in Private Water Supplies: Relation to Bedrock Geology. Int J Environ Res Public Health. 2017 Dec 1;14(12). pii: E1490. doi: 10.3390/ijerph14121490.
Approximately one million people in the UK are served by private water supplies (PWS) where main municipal water supply system connection is not practical or where PWS is the preferred option. Chronic exposure to contaminants in PWS may have adverse effects on health. South West England is an area with elevated arsenic concentrations in groundwater and over 9000 domestic dwellings here are supplied by PWS. There remains uncertainty as to the extent of the population exposed to arsenic (As), and the factors predicting such exposure. We describe a hazard assessment model based on simplified geology with the potential to predict exposure to As in PWS. Households with a recorded PWS in Cornwall were recruited to take part in a water sampling programme from 2011 to 2013. Bedrock geologies were aggregated and classified into nine Simplified Bedrock Geological Categories (SBGC), plus a cross-cutting “mineralized” area. PWS were sampled by random selection within SBGCs and some 508 households volunteered for the study. Transformations of the data were explored to estimate the distribution of As concentrations for PWS by SBGC. Using the distribution per SBGC, we predict the proportion of dwellings that would be affected by high concentrations and rank the geologies according to hazard. Within most SBGCs, As concentrations were found to have log-normal distributions. Across these areas, the proportion of dwellings predicted to have drinking water over the prescribed concentration value (PCV) for As ranged from 0% to 20%. From these results, a pilot predictive model was developed calculating the proportion of PWS above the PCV for As and hazard ranking supports local decision making and prioritization. With further development and testing, this can help local authorities predict the number of dwellings that might fail the PCV for As, based on bedrock geology. The model presented here for Cornwall could be applied in areas with similar geologies. Application of the method requires independent validation and further groundwater-derived PWS sampling on other geological formations.