Potential of microalgae as biopesticides to contribute to sustainable agriculture and environmental development

The loss of yields from agricultural production due to the presence of pests has been treated over the years with synthetic pesticides, but the use of these substances negatively affects the environment and presents health risks for consumers and animals. The development of agroecological systems using biopesticides represents a safe alternative that contributes to the reduction of agrochemical use and sustainable agriculture. Microalgae are able to biosynthesize a number of metabolites with potential biopesticidal action and can be considered potential biological agents for the control of harmful organisms to soils and plants. The present work aims to provide a critical perspective on the consequences of using synthetic pesticides, offering as an alternative the biopesticides obtained from microalgal biomass, which can be used together with the implementation of environmentally friendly agricultural systems.     

Comparison and Evaluation of Gridded Precipitation Datasets in a Kansas Agricultural Watershed Using SWAT

Gridded precipitation datasets are becoming a convenient substitute for gauge measurements in hydrological modeling; however, these data have not been fully evaluated across a range of conditions. We compared four gridded datasets (Daily Surface Weather and Climatological Summaries [DAYMET], North American Land Data Assimilation System [NLDAS], Global Land Data Assimilation System [GLDAS], and Parameter‐elevation Regressions on Independent Slopes Model [PRISM]) as precipitation data sources and evaluated how they affected hydrologic model performance when compared with a gauged dataset, Global Historical Climatology Network‐Daily (GHCN‐D). Analyses were performed for the Delaware Watershed at Perry Lake in eastern Kansas. Precipitation indices for DAYMET and PRISM precipitation closely matched GHCN‐D, whereas NLDAS and GLDAS showed weaker correlations. We also used these precipitation data as input to the Soil and Water Assessment Tool (SWAT) model that confirmed similar trends in streamflow simulation. For stations with complete data, GHCN‐D based SWAT‐simulated streamflow variability better than gridded precipitation data. During low flow periods we found PRISM performed better, whereas both DAYMET and NLDAS performed better in high flow years. Our results demonstrate that combining gridded precipitation sources with gauge‐based measurements can improve hydrologic model performance, especially for extreme events.     

Urban wild meat consumption and trade in central Amazonia

The switch from hunting wild meat for home consumption to supplying more lucrative city markets in Amazonia can adversely affect some game species. Despite this, information on the amounts of wild meat eaten in Amazonian cities is still limited. We estimated wild meat consumption rates in 5 cities in the State of Amazonas in Brazil through 1046 door-to-door household interviews conducted from 2004 to 2012. With these data, we modeled the relationship between wild meat use and a selection of socioeconomic indices. We then scaled up our model to determine the amounts of wild meat likely to be consumed annually in the 62 urban centers in central Amazonia. A total of 80.3% of all interviewees reported consuming wild meat during an average of 29.3 (CI 11.6) days per year. Most wild meat was reported as bought in local markets (80.1%) or hunted by a family member (14.9%). Twenty-one taxa were cited as consumed, mostly mammals (71.6%), followed by reptiles (23.2%) and then birds (5.2%). The declared frequency of wild meat consumption was positively correlated with the proportion of rural population as well as with the per capita gross domestic product of the municipality (administrative divisions) where the cities were seated. We estimated that as much as 10,691 t of wild meat might be consumed annually in the 62 urban centers within central Amazonia, the equivalent of 6.49 kg per person per year. In monetary terms, this amounts to US$21.72 per person per year or US$35.1 million overall, the latter figure is comparable to fish and timber production in the region. Given this magnitude of wild meat trade in central Amazonia, it is fundamental to integrate this activity into the formal economy and actively develop policies that allow the trade of more resilient taxa and restrict trade in species sensitive to hunting.     

The developmental neurotoxicity of legacy vs. contemporary polychlorinated biphenyls (PCBs): similarities and differences

Although banned from production for decades, PCBs remain a significant risk to human health. A primary target of concern is the developing brain. Epidemiological studies link PCB exposures in utero or during infancy to increased risk of neuropsychiatric deficits in children. Nonclinical studies of legacy congeners found in PCB mixtures synthesized prior to the ban on PCB production suggest that non-dioxin-like (NDL) congeners are predominantly responsible for the developmental neurotoxicity associated with PCB exposures. Mechanistic studies suggest that NDL PCBs alter neurodevelopment via ryanodine receptor-dependent effects on dendritic arborization. Lightly chlorinated congeners, which were not present in the industrial mixtures synthesized prior to the ban on PCB production, have emerged as contemporary environmental contaminants, but there is a paucity of data regarding their potential developmental neurotoxicity. PCB 11, a prevalent contemporary congener, is found in the serum of children and their mothers, as well as in the serum of pregnant women at increased risk for having a child diagnosed with a neurodevelopmental disorder (NDD). Recent data demonstrates that PCB 11 modulates neuronal morphogenesis via mechanisms that are convergent with and divergent from those implicated in the developmental neurotoxicity of legacy NDL PCBs. This review summarizes these data and discusses their relevance to adverse neurodevelopmental outcomes in humans.

     

Environmental forensic investigation of the air pollution from a cement manufacturing unit

Abstract

Cement manufacturing is a process that results in the emission of significant quantities of suspended particulate matter (SPM) to the ambient air. An environmental forensic investigation was carried out in the surroundings of a major cement manufacturing unit at a place called Coimbatore in the southern Indian state of Tamil Nadu. The investigation was carried out to identify the contribution of the cement manufacturing unit to the SPM concentration of the surrounding air environment. The sampling points’ selection and sample collection were done following the principles outlined in the INTERPOL Manual for Pollution Crime Forensic Investigation. On-site monitoring of the air samples was carried out using Mini Laser Aerosol Spectrometer (GRIMM, Mini-LAS Model 11R). The instrument was capable of measuring particles ranging from 0.25 to 32 µm and classifying them into 31 size channels. The test results at majority of the monitoring locations were well above the limits specified in the National Ambient Air Quality Standards of India. Microscopic studies of the dust samples were carried out for surface texture and particle shape. The spatial distribution of particles was analysed using geographic information system (GIS) for the visual identification of the extent of the pollution by keeping the cement factory as the focal point. The results from the GIS and microscopic analysis established the role of the cement factory in the particulate matter pollution of its surroundings, specifically in the areas North-West of the factory. The successfully adopted procedure can serve as a guideline for the environmental forensic investigation of similar pollution incidences.     

Use of Predictive Weather Uncertainties in an Irrigation Scheduling Tool Part II: An Application of Metrics and Adjoints

We apply predictive weather metrics and land model sensitivities to improve the Colorado State University Water Irrigation Scheduler for Efficient Application (WISE). WISE is an irrigation decision aid that integrates environmental and user information for optimizing water use. Rainfall forecasts and verification performance metrics are used to estimate predictive rainfall probabilities that are used as input data within the irrigation decision aid. These input data errors are also used within a land model sensitivity study to diagnose important prognostic water movement behaviors for irrigation tool development purposes simultaneously performing the analysis in space and time. Thus, important questions such as “how long can a crop water application be delayed while maintaining crop yield production?” are addressed by evaluating crop growth stage interactions as a function of soil depth (i.e., space), rainfall events (i.e., time), and their probabilistic uncertainties. Editor’s note : This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.     

Development,larvicide activity,and toxicity in nontarget species of the Croton linearis Jacq essential oil nanoemulsion

Environmental Science and Pollution Research - In this study, the essential oil (EO) from leaves of Croton linearis Jacq was extracted and characterized by GC/MS. The EO hydrophilic-lipophilic...     

Influence of Chronic Man-made Pollution on Bromus inermis Genome Size

Russian Journal of Ecology - Technogenic pollution can accelerate microevolutionary processes in natural populations. We estimated the nuclear DNA content of 10 Bromus inermis Leyss. samples from...     

Trauma demography and clinical epidemiology of motorcycle crash–related head injury in a neurosurgery practice in an African developing country

Objective: Though motor vehicle crashes (MVCs) were the main cause of head trauma from road traffic injuries (RTIs), motorcycle crashes (MCCs) are now a major cause of RTI-related head injury (HI) in many developing countries.

Methods: Using a prospective database of HIs from a neurosurgical practice in a sub-Saharan African developing country, a cross-sectional survey was conducted for the trauma demography and clinical epidemiology of this MCC-related HI.

Results: Motorcycle crashes accounted for 57% (473/833) of all RTI-related HIs in this registry. The victims, with a mean age of 33.1 years (SD = 18.3), consisted mainly of males (83.1%), those of low socioeconomic status (>90%), and those aged between 20 and 40 years old (56%). MCCs involved only riders in 114 cases (114/473, 32.1%), of which 69% were motorcycle–motorcycle crashes. The HI was moderate–severe in 50.8%; clinical symptomatology of significant HI included loss of consciousness (92%), anisocoria (35%), Abbreviated Injury Scale head (AIS–head) score > 3 (28%), and CT-Rotterdam score > 3 (30%). Extracranial systemic injury involved the limbs most frequently, with an Injury Severity Score (ISS) >25 in 49%. The fatality rate was 24%.

MCC-related HI among pedestrian victims involved more vulnerable age groups (the young and elderly) but have lower mean ISS compared to motorcycle passengers (mean ISS = 23.5 [11.6] vs. 27.4 [13.0]; 95% confidence interval [CI], 1.27–6.49; P = .004). In addition, compared to a contemporary cohort of MVC-related HIs in our registry, MCC victims were older (mean age 34.8 years [18.0] vs. 30.8 [18.4]; P = .002); had higher proportions of certain extracranial trauma like long bone fractures (71 vs. 29%; P = .02); and suffered fewer surgical brain lesions (25.5 vs. 17.2%; P = .004).

Conclusions: Motorcycle crashes are now a significant threat to the heads, limbs, and lives of vulnerable road users in developing countries.