Purpose: There has been a growing concern about air quality because in recent years, industrial and vehicle emissions have resulted in unsatisfactory human health conditions. There is an urgent need for the measurements and estimations of particulate pollutants levels, especially in urban areas. As a contribution to this issue, the purpose of this paper is to use data from measured concentrations of particulate matter and meteorological conditions for the predictions of PM₁₀. Design/methodology/approach: The procedure included daily data collection of current PM₁₀ concentrations for the city of São Carlos-SP, Brazil. These data series enabled to use an estimator based on artificial neural networks. Data sets were collected using the high-volume sampler equipment (VFA-MP10) in the period ranging from 1997 to 2006 and from 2014 to 2015. The predictive models were created using statistics from meteorological data. The models were developed using two neural network architectures, namely, perceptron multilayer (MLP) and non-linear autoregressive exogenous (NARX) inputs network. Findings: It was observed that, over time, there was a decrease in the PM₁₀ concentration rates. This is due to the implementation of more strict environmental laws and the development of less polluting technologies. The model NARX that used as input layer the climatic variables and the PM₁₀ of the previous day presented the highest average absolute error. However, the NARX model presented the fastest convergence compared with the MLP network. Originality/value: The presentation of a given PM₁₀ concentration of the previous day improved the performance of the predictive models. This paper brings contributions with the NARX model applications.

The accelerated growth of cyanobacteria in water bodies is a global critical environmental issue caused by continuous discharges of effluents into the environment that are rich in phosphorus and nitrogen. So, cyanobacteria have found propitious conditions for proliferation, provoking significant ecological imbalances. Cyanobacteria produce cyanotoxins, which are harmful to life, and compounds like 2-methylisoborneol and geosmin that affect water's taste and odor. This study analyzed a long-term database of important environmental parameters from a tropical reservoir in São Paulo State, Brazil. The statistical methods of correlation matrices and principal component analysis were used. Data analysis revealed a significant relationship between cyanobacteria growth and high levels of phosphate and nitrogen. Furthermore, positive correlations were found among concentrations of biocidal elements like antimony, arsenic, and selenium related to cyanobacterial bloomings. These correlations can be attributed to agricultural wastewaters and/or possible algicide used to control these microorganisms.

Environmental monitoring indicators guide the decision-making process of public policies. Bioassays using seeds are a potential tool to evaluate water quality indicators, since their results can be expressed in a phytotoxicity scale, the tests are quite simple, relatively fast, and low cost. In this context, the objectives of this study were to evaluate the phytotoxicity of Rio Grande reservoir waters and its relationship with environmental variables. In Brazil, this reservoir is an important water source for the public supply of the Metropolitan Region of São Paulo. In 2018, subsurface and bottom water were collected in four sampling campaigns. The environmental variables were measured both in field and at the laboratory. In field, it was used a multiprobe of ammonium (N-NH4+), electrical conductivity (EC), nitrate (N-NO3-), pH, dissolved oxygen (DO) and water temperature (T); and, at the laboratory, of total nitrogen (TN) and total phosphorus (TP). Bioassays using seeds of cucumber (Cucumis sativus), garden cress (Lepidium sativum), and mustard (Sinapis alba) were performed (n = 6). The bioassays consisted of incubating the seeds and the water samples on Petri dishes at (20º ± 2 ºC) in the absence of light for 3 days, in case of S. alba, and for 4 days, in case of C. sativus and L. sativum. ISO water was used as a control. Afterward, the number of germinated seeds and the roots' length were determined to calculate the Germination Index (GI). Statistical analyses were performed, such as the Shapiro-Wilk test, the analysis of variance (ANOVA) and the Tukey test, to verify the differences between the seeds' responses to the environmental variables. The Principal Component Analysis (PCA) was applied to the environmental variables and the GI to identify the most representative variables. This study indicated that, in May of 2018, the subsurface water was moderately phytotoxic to L. sativum (GI = 61.3 ± 8.2%); and the bottom water was very phytotoxic to L. sativum (GI = 23.6 ± 14.5%) and S. alba (26.2 ± 12.0%). In all other sampling campaigns, the water samples potentiated seed germination, which can be related to nutrients concentrations The L. sativum proved to be more sensitive, due to the lower degrees of data dispersion and the higher degrees of toxicity when compared to the other analysed seeds.

Corrigendum: Higher Abundance of Sediment Methanogens and Methanotrophs Do Not Predict the Atmospheric Methane and Carbon Dioxide Flows in Eutrophic Tropical Freshwater Reservoirs