Due to their systemic character and high efficacy to insect controls, neonicotinoid insecticides (neonics) have been widely used in global agriculture since its introduction in early 1990. Recent studies have indicated that neonics may be ubiquitous, have longer biological half-lives in the environment once applied, and therefore implicitly suggested the increasing probability for human exposure to neonics. Despite of neonics’ persistent characters and widespread uses, scientific literature in regard of pathways in which human exposure could occur is relatively meager. In this review, we summarized results from peer-reviewed articles published prior to 2017 that address potential human exposures through ingestion and inhalation, as well as results from human biomonitoring studies. In addition, we proposed the use of relative potency factor approach in order to facilitate the assessment of concurrent exposure to a mixture of neonics with similar chemical structures and toxicological endpoints. We believe that the scientific information that we presented in this review will aid to future assessment of total neonic exposure and subsequently human health risk characterization.

Fine particulate matter (PM₂.₅) pollution has been a major issue in many countries. Considerable studies have demonstrated that PM₂.₅ pollution is a regional issue, but little research has been done to investigate the regional extent of PM₂.₅ pollution or to define areas in which PM₂.₅ pollutants interact. To allow for a better understanding of the regional nature and spatial patterns of PM₂.₅ pollution, This study proposes a novel framework for delineating regional boundaries of PM₂.₅ pollution. The framework consists of four steps, including cross-correlation analysis, time-series clustering, generation of Voronoi polygons, and polygon smoothing using polynomial approximation with exponential kernel method. Using the framework, the regional PM₂.₅ boundaries for China are produced and the boundaries define areas where the monthly PM₂.₅ time series of any two cities show, on average, more than 50% similarity with each other. These areas demonstrate straightforwardly that PM₂.₅ pollution is not limited to a single city or a single province. We also found that the PM₂.₅ areas in China tend to be larger in cold months, but more fragmented in warm months, suggesting that, in cold months, the interactions between PM₂.₅ concentrations in adjacent cities are stronger than in warmer months. The proposed framework provides a tool to delineate PM₂.₅ boundaries and identify areas where PM₂.₅ pollutants interact. It can help define air pollution management zones and assess impacts related to PM₂.₅ pollution. It can also be used in analyses of other air pollutants.

Concentrations of 26 brominated flame retardants (BFRs), including 19 polybrominated diphenyl ethers congeners (PBDEs), 3 isomers of hexabromocyclododecanes (HBCDs), and 4 alternative BFRs (alt-BFRs; hexabromobenzene, pentabromotoluene, 1,2-bis(2,4,6-tribromphenoxy)ethane, and decabromodiphenylethane) were determined in outdoor settled dust and pine needles collected across mainland China. BFRs were extensively found in the two matrices, with mean total concentrations at 4090 and 314 ng/g dry weight (dw), in dust and pine needles, respectively. The total BFRs concentrations in dust significantly varied among three mixed-land-use categories, with mean concentrations of 74.3, 1284, and 25,525 ng/g dw in rural, urban, and point source areas, respectively. For PBDE congeners, dust samples contained predominantly BDE-209 (69.2% of the total BFRs), whereas lower brominated PBDEs such as BDE-28 (19.7%), −47 (11.0%), and −99 (12.2%) accounted for higher proportions in pine needles. Spatial distribution of BFRs showed distinct geographical signatures with the highest levels found in South Central China. Application of McLachlan's framework to our data suggested that the uptake of BFRs in pine needles was controlled primarily by kinetically limited gaseous deposition and by particle-bound deposition. Assessment on human exposure to BFRs through outdoor dust ingestion revealed a low risk for Chinese adults and toddlers.

In recent years, numerous studies have reported the prevalence of organic micropollutants in natural waters. There is an increasing interest in assessing the occurrence and transport of these contaminants in groundwater because a large number of people in the United States rely on groundwater for their drinking water. However, commonly used mass-spectrometry-based analytical methods are expensive and time-consuming. The enzyme-linked immunosorbent assay (ELISA) method offers an inexpensive analytical alternative that provides semi-quantitative results in a relatively quick timeframe. We investigated the use of ELISA for two commonly detected micropollutants, sulfamethoxazole (SMX) and carbamazepine (CBZ), in groundwater collected as part of two different studies, one in Minnesota and the other in Iowa. The ELISA results were compared with two mass-spectrometry-based methods: (1) direct aqueous injection-high performance liquid chromatography/tandem mass spectrometry (HPLC) and (2) online solid-phase extraction with liquid chromatography/electrospray ionization-mass spectrometry (SPE LC). Differences in SMX and CBZ observations between ELISA and both HPLC and SPE LC were analyzed using the Paired Prentice-Wilcoxon test. Estimates of bias and limits of agreement between paired observations also were calculated. The SMX determinations by ELISA yielded results that were 30 and 14% greater than HPLC and SPE LC, respectively. The CBZ determinations by ELISA yielded results that were 25 and 9% greater than HPLC and SPE LC, respectively. The ELISA determinations were in presence-absence agreement with HPLC for 83% of samples for SMX and CBZ; and with SPE LC for 76 and 80% of samples for SMX and CBZ, respectively. Results indicate that ELISA for SMX and CBZ is a reliable and cost effective screening-tool alternative to more commonly used mass spectrometry-based analytical methods.

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM₂.₅ can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM₂.₅, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM₂.₅ levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008–2011 and 2014–2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM₂.₅ concentrations varied from 0.98 to 54.2 μg m⁻³, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM₂.₅, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM₂.₅ exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Understanding the effects of urban morphology on urban environmental noise (UEN) at a regional scale is crucial for creating a pleasant urban acoustic environment. This study seeks to investigate how the urban morphology influences the UEN in the Shenzhen metropolitan region of China, by employing remote sensing and geographic information data. The UEN in this study consists of not only regional environmental noise (RN), but also traffic noise (TN). The experimental results reveal the following findings: 1) RN is positively correlated with the nighttime light intensity (NTL) and land surface temperature (LST) (p < 0.05). More interestingly, landscape composition and configuration can also significantly affect RN. For instance, urban vegetation can mitigate the RN (r = −0.411, p < 0.01). There is a reduced RN effect when fewer buildings exist in an urban landscape, in terms of the positive relationship between building density and RN (r = 0.188, p < 0.01). Given the same percentage of building area, buildings are more effective at reducing noise when they are distributed across the urban scenes, rather than being spatially concentrated (r = −0.205, p < 0.01). 2) TN positively relates to large (r = 0.520, p < 0.01) and small–medium (r = 0.508, p < 0.01) vehicle flow. In addition, vegetation along or near roads can alleviate the TN effect (r = −0.342, p < 0.01). TN can also become more severe in urban landscapes where there is higher road density (r = 0.307, p < 0.01). 3) Concerning the urban functional zones, traffic land is the greatest contributor to urban RN, followed by mixed residential and commercial land. The findings revealed by this research will indicate how to mitigate UEN.

A Community Multiscale Air Quality (CMAQ) model with source-oriented lumped SAPRC-11 (S11L) photochemical mechanism and secondary organic aerosol (SOA) module was applied to determine the contributions of anthropogenic and biogenic sources to SOA concentrations in China. A one-year simulation of 2013 using the Multi-resolution Emission Inventory for China (MEIC) shows that summer SOA are generally higher (10–15 μg m−3) due to large contributions of biogenic (country average 60%) and industrial sources (17%). In winter, SOA formation was mostly due to anthropogenic emissions from industries (40%) and residential sources (38%). Emissions from other countries in southeast China account for approximately 14% of the SOA in both summer and winter, and 46% in spring due to elevated open biomass burning in southeast Asia. The Regional Emission inventory in ASia v2.1 (REAS2) was applied in this study for January and August 2013. Two sets of simulations with the REAS2 inventory were conducted using two different methods to speciate total non-methane carbon into model species. One approach uses total non-methane hydrocarbon (NMHC) emissions and representative speciation profiles from the SPECIATE database. The other approach retains the REAS2 speciated species that can be directly mapped to S11L model species and uses source specific splitting factors to map other REAS2 lumped NMHC species. Biogenic emissions are still the most significant contributor in summer based on these two sets of simulations. However, contributions from the transportation sector to SOA in January are predicted to be much more important based on the two REAS2 emission inventories (∼30–40% vs. ∼5% by MEIC), and contributions from residential sources according to REAS2 was much lower (∼21–24% vs. ∼42%). These discrepancies in source contributions to SOA need to be further investigated as the country seeks for optimal emission control strategies to fight severe air pollution.

Frequencies and durations of blooms of cyanobacteria are increasing. Some cyanobacteria can produce cyanotoxins including microcystins (MCs). MCs are the most common toxic products of hazardous algal blooms (HABs), with the greatest potential for exposure and to cause toxicity. Recently, MCs have been shown to disrupt endocrine functions. In this study, for the first time, effects of MC-LR on the hypothalamic-pituitary-inter-renal (HPI) axis during early embryonic development (embryos/larvae) of zebrafish (Danio rerio), were investigated. Embryos/larvae of zebrafish were exposed to 1, 10, 100, or 300 μg MC-LR/L during the period of 4–168 h post-fertilization (hpf). Exposure to 300 μg MC-LR/L resulted in significantly greater concentrations of whole-body cortisol than those in controls. Expressions of genes along the HPI axis and mineralocorticoid receptor (MR-) and glucocorticoid receptor (GR-) centered gene networks were evaluated by use of quantitative real-time PCR. Expression of mRNA for crh was significantly down-regulated by exposure to 300 μg MC-LR/L, while expressions of crhbp, crhr1, and crhr2 were significantly up-regulated, relative to controls. MC-LR caused significantly lesser levels of mRNA for steroidogenic genes including hmgra, star, and cyp17, but expression of mRNA for hsd20b was significantly greater than that of controls. Treatment with MC-LR also altered profiles of transcription of MR- and GR-centered gene networks, which might result in multiple responses. Taken together, these results demonstrated that MC-LR affected the corticosteroid-endocrine system of larvae of zebrafish. This study provided valuable insights into molecular mechanisms behind potential toxicity and endocrine disruption of MCs.

Genetic and epigenetic changes were investigated in chronically irradiated Scots pine (Pinus sylvestris L.) populations from territories that were heavily contaminated by radionuclides as result of the Chernobyl Nuclear Power Plant accident. In comparison to the reference site, the genetic diversity revealed by electrophoretic mobility of AFLPs was found to be significantly higher at the radioactively contaminated areas. In addition, the genome of pine trees was significantly hypermethylated at 4 of the 7 affected sites.