Association between PM2.5 and mortality of stomach and colorectal cancer in Xi’an: a time-series study

Environmental Science and Pollution Research - Globally, fine particulate matter has been associated with several health problems including cancer. However, most studies focused mainly on lung...     

The effect of an industrial effluent on an urban stream benthic community: water quality vs habitat quality

We studied the effect of an industrial effluent on the water quality, habitat quality, and benthic macroinvertebrates of an urban stream in southwestern Michigan (USA). The effluent affected water quality by raising in-stream temperatures 13-18 degree C during colder months and carrying high amounts of iron (> 20 x higher than ambient) that covered the streambed. The effluent also affected habitat conditions by increasing total stream discharge by 50-150%, causing a significant change in substrate and flow conditions. We used three methods to collect benthic macroinvertebrates in depositional and erosional habitats and to understand the relative importance of habitat quality and water quality alterations. Macroinvertebrate response variables included taxonomic richness, abundance, and proportional abundance of sensitive taxonomic groups. Results indicated that the effluent had a positive effect on macroinvertebrate communities by increasing the quantity of riffle habitat, but a negative effect on macroinvertebrate communities by reducing water quality. Results illustrated the need for careful consideration of habitat quality and water quality in restoration or remediation programs.     

On the use of log-transformation vs. nonlinear regression for analyzing biological power laws

Power-law relationships are among the most well-studied functional relationships in biology. Recently the common practice of fitting power laws using linear regression (LR) on log-transformed data has been criticized, calling into question the conclusions of hundreds of studies. It has been suggested that nonlinear regression (NLR) is preferable, but no rigorous comparison of these two methods has been conducted. Using Monte Carlo simulations, we demonstrate that the error distribution determines which method performs better, with NLR better characterizing data with additive, homoscedastic, normal error and LR better characterizing data with multiplicative, heteroscedastic, lognormal error. Analysis of 471 biological power laws shows that both forms of error occur in nature. While previous analyses based on log-transformation appear to be generally valid, future analyses should choose methods based on a combination of biological plausibility and analysis of the error distribution. We provide detailed guidelines and associated computer code for doing so, including a model averaging approach for cases where the error structure is uncertain.