Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

Elevated manganese (Mn) in drinking water has been reported worldwide. While, naturally occurring Mn in groundwater is generally the major source, anthropogenic contamination by Mn-containing fungicides such as mancozeb may also occur. The main objective of this study was to examine factors associated with Mn and ethylenethiourea (ETU), a degradation product of mancozeb, in drinking water samples from villages situated near banana plantations with aerial spraying of mancozeb. Drinking water samples (n = 126) were obtained from 124 homes of women participating in the Infants' Environmental Health Study (ISA, for its acronym in Spanish), living nearby large-scale banana plantations. Concentrations of Mn, iron (Fe), arsenic (As), lead (Pb), cadmium (Cd) and ethylenethiourea (ETU), a degradation product of mancozeb, were measured in water samples. Only six percent of samples had detectable ETU concentrations (limit of detection (LOD) = 0.15 μg/L), whereas 94% of the samples had detectable Mn (LOD = 0.05 μg/L). Mn concentrations were higher than 100 and 500 μg/L in 22% and 7% of the samples, respectively. Mn was highest in samples from private and banana farm wells. Distance from a banana plantation was inversely associated with Mn concentrations, with a 61.5% decrease (95% CI: −97.0, −26.0) in Mn concentrations for each km increase in distance. Mn concentrations in water transported with trucks from one village to another were almost 1000 times higher than Mn in water obtained from taps in houses supplied by the same well but not transported, indicating environmental Mn contamination. Elevated Mn in drinking water may be partly explained by aerial spraying of mancozeb; however, naturally occurring Mn in groundwater, and intensive agriculture may also contribute. Drinking water risk assessment for mancozeb should consider Mn as a health hazard. The findings of this study evidence the need for health-based World Health Organization (WHO) guidelines on Mn in drinking water.

A pesticide monitoring study including 80 and 60 active ingredients (in surface waters and sediments, respectively) was carried out in a river basin in Costa Rica during 2007–2012. A special emphasis was given on the exceptional ecological conditions of the tropical agro-ecosystem and the pesticide application strategies in order to establish a reliable monitoring network. A total of 135 water samples and 129 sediment samples were collected and analyzed. Long-term aquatic ecotoxicological risk assessment based on risk quotient in three trophic levels was conducted. Short-term risk assessment was used to calculate the toxic unit and prioritization of sampling sites was conducted by the sum of toxic units in both aquatic and sediment compartments. Dimethoate (61.2 μg/L), propanil (30.6 μg/L), diuron (22.8 μg/L) and terbutryn (4.8 μg/L) were detected at the highest concentrations in water samples. Carbendazim and endosulfan were the most frequently detected pesticides in water and sediment samples, respectively. Triazophos (491 μg/kg), cypermethrin (71.5 μg/kg), permethrin (47.8 μg/kg), terbutryn (38.7 μg/kg), chlorpyrifos (18.2 μg/kg) and diuron (11.75 μg/kg) were detected at the highest concentrations in sediment samples. The pesticides carbendazim, diuron, endosulfan, epoxyconazole, propanil, triazophos and terbutryn showed non-acceptable risk even when a conservative scenario was considered. Sum TUsite higher than 1 was found for one and two sampling sites in water and sediment compartments, respectively, suggesting high acute toxicity for the ecosystem.Exceptional ecological conditions of the tropical agro-ecosystem affect the fate of pesticides in water and sediment environment differently than the temperate one.

This study was a baseline with quantitative data of marine litter along the Gulf of Nicoya, Costa Rica. The objective of the study was to quantify marine litter and its association with human activities in this estuarine gulf. A total of fourteen sandy beaches were cataloged by the degree of urbanization, tourism intensity, beach cleaning programs, and tributary rivers as possible drivers of marine litter presence. The items of the marine litter were separated and weighted by type. Analysis by the clean coastal index (CCI) and multivariate statistics were applied to find spatial patterns in marine litter in the gulf. On beaches with the highest touristic activity, cigarette butts and straws were the main components. Locations with river plume influence, less frequent cleanup, or waste cans showed more bottles, plastic parts, and sanitary waste than beaches in other conditions. A beach in a fisherman town had recently utilized plastic bags, household goods, and boat parts in the marine litter. A wildlife refuge beach showed only small plastic and coffee foam cup fragments that came with currents from other points in the estuary. River basin management, solid waste disposal programs, and environmental education to avoid single-use items combined with correct waste disposal are needed to reduce marine litter in tropical countries focused on ecological tourism.

This study analyzed the profiles of polycyclic aromatic hydrocarbons (PAHs) in filterable PM2.5 particles collected from a total of 71 boilers and 22 indirect type furnaces that burn liquid and biomass fuels in the Metropolitan Area of Costa Rica, from February 2014 to November 2015. Modified method NIOSH 5506 was used to analyze PAHs content present in the filter samples. The average concentration of PM2.5 showed values between 18 and 735 mg m−3, based on the source and fuel type used, while the total PAHs in the PM2.5 fraction ranged 1.02–592 μgm−3. For biomass boilers, the most abundant species were Benzo[g,h,i]perylene (BghiP) (35.7–46.5%), Indeno[1,2,3-cd]pyrene (IND) (20.6–27.1%), Benzo[a]pyrene (BaP) (5.2–14.7%) and Dibenzo[a,h]anthracene (DBA) (3.2–13.9%), while for liquid fuels IND (12.8–20.5%), BghiP (7.9–21.2%), Fluoranthene (Flu) (14.5–21.3%) and Pyrene (Pyr) (9.8–14.5%) prevailed. The particles from biomass furnace emissions present higher concentrations of PAHs classified by the U.S. EPA as probable human carcinogens causing a greater health risk than other fuels. Among the diagnostic concentration ratios examined, only BaP/(BaP+Chr), BaA/Chr, BaA/BaP and Pyr/BaP coefficients demonstrated codependency on the type of fuel used.

Moín, on the Caribbean coast of Costa Rica, is a multi-use coastal zone with a variety of human activities that can cause metal pollution. With the purpose of assessing the current environmental burden due to heavy metal presence in the marine environment of Moín, and their bioaccumulation in organisms of the nearby coral reef, we determined seven metals in samples of bottom sediments, macroalgae (Cryptonemia crenulata) and sponge (Cinachyrella kuekenthali). The results were compared with samples from the southern Caribbean, an area with little human activity. Using ICP-MS, results showed a concentration range for sediments Mn > Cu > Zn > Cr > Ni > Pb > Cd, algae Mn > Cu > Zn > Ni > Cr > Pb > Cd and sponge Mn > Cu > Zn > Ni > Cr > Cd > Pb, relatively low concentrations overall and no differences observed between sites. Bioconcentration factor > 1 was determined for Cd, Cu, Ni and Zn, while concentrations in sediments were below the SQG thresholds. Our study provides the first data on metal concentrations in a macroalgae and a sponge from the Costa Rican Caribbean.

Polycyclic aromatic hydrocarbons (PAHs) concentrations in PM10 and PM2.5 particles were measured at 14 monitoring sites (12 for PM10 and 2 for PM2.5), located in the Metropolitan Area of Costa Rica, from January to November 2013. High-volume air samplers with pretreated quartz filters were used to collect the particles. The analytical determination was carried out using high-performance liquid chromatography (HPLC). The most abundant PAHs were benzo[a]anthracene, indeno(1,2,3-cd)pyrene, dibenz[a,h]anthracene and acenaphthylene. Ratios obtained by correlating the concentration of some PAHs, both PM10 and PM2.5, suggest that gasoline and diesel vehicles are the main sources in the area being studied. This is consistent with the results obtained when applying the positive matrix factorization (PMF) analysis, since vehicles accounted for 62–74% of total emissions in the area; burning wood fuel was the second source of emissions, contributing between 7 and 15%; and road dust was third, with almost 8%.

PM10 and PM2.5 levels, concentrations of major ionic components, trace elements, and organic and elemental carbon were evaluated from samples collected in 4 sites (industrial, commercial and residential zones) located in the metropolitan area of Costa Rica. The annual mean PM levels were higher in high traffic–commercial (HE–01) and industrial (BE–02) sites, 55 μg m–3 and 52 μg m–3 for PM10 and 37 μg m–3 and 36 μg m–3 for PM2 5, respectively. The major components of PM25 were organic matter (OM) and elemental carbon (EC) (44.5–69.9%), and secondary ions (16.1–27.2%), whereas the major components of PM10 were OM+EC (32.7–59.4%), crustal material (23.5–35.6%) and secondary ions (11.4–26.9%). For the most of the sampling sites, PM10 and PM2.5 concentrations were lower during the dry season and increased gradually in the rainy season due to wind patterns. PMF model identified 8 principle sources for PM10 and PM2.5 in the industrial site (crustal, secondary sulfate, secondary nitrate, secondary organic, traffic, sea–salt aerosols, industrial and oil combustion), 6 and 5 sources in commercial and residential sites, respectively. The source contributions showed a clear seasonal pattern for all the sites.

This study investigated the performance of aerobic windrow systems by using coffee by-products and green waste to reduce gaseous emissions. Thereafter, a comparison with the current treatment and gaseous emissions at a Coffee Mill in Costa Rica was made. Two different studies where performed in Germany (pile I and II) and one study in a Coffee Mill in Costa Rica (pile III). Temperature, water content, and pH were the key parameters controlled over 35 days in all the systems. Moreover, CH₄ emission rates were quantified by a FTIR and by a portable gas detector device where the emissions reached values 100 times higher when coffee by-products as a unique material for the composting process was used. Results show that highest emission rates during the composting process for pile I was 0.007 g(m²)⁻¹ h⁻¹, for pile II 0.006 g(m²)⁻¹ h⁻¹, and for pile III 3.1 g(m²)⁻¹ h⁻¹. It was found that CH₄ emissions could be avoided if the mixture and the formation of the windrow piles were performed following the key parameter for composting, and the usage of additional material is used. With this, the reduction of CH₄ emissions at the Mill in Costa Rica could be achieved in the future.

Chlorination is one of the most important stages in the treatment of drinking water due to its effectiveness in the inactivation of pathogenic organisms. However, the reaction between chlorine and natural organic matter (NOM) generates harmful disinfection by-products (DBPs), such as trihalomethanes (THMs). In this research, drinking water quality data was collected from the distribution networks of 19 rural and semi-urban systems that use water sources as springs, surfaces, and a mixture of both, in three provinces of Costa Rica from April 2018 to September 2019. Twelve models were developed from four data sets: all water sources, spring, surface, and a mixture of spring and surface waters. Linear, logarithmic, and exponential multivariate regression models were developed for each data set to predict the concentration of total trihalomethanes (TTHMs) in the distribution networks. Concentrations of TTHMs were found between < 0.20 and 91.31 µg/L, with chloroform being the dominant species accounting for 62% of TTHMs on average. Turbidity, free residual chlorine, total organic carbon (TOC), dissolved organic carbon (DOC), and ultraviolet absorbance at 254 nm (UV₂₅₄) showed a significant correlation with TTHMs. In all the data sets the linear models presented the best goodness-of-fit and were moderately robust. Four models, the best of each data set, were validated with data from the same systems, and, according to the criteria of R², standard error (SE), mean square error (MSE), and mean absolute error (MAE), spring water and mixed spring/surface water models showed a satisfactory level of explanation of the variability of the data. Moreover, the models seem to better predict TTHM concentrations below 30 µg/L. These models were satisfactory and could be useful for decision-making in drinking water supply systems.