PM2.5 monitoring during a 10-year period: relation between elemental concentration and meteorological conditions.

Four monitoring campaigns between the years 2009 and 2018 were conducted in Córdoba City, Argentina, to detect toxic metals in PM2.5 samples. The concentrations of As, Cd, Pb, Cu, Cr, Mn, Hg, Ni, and Zn, together with several other elements, were measured. The average metal concentrations followed the order: Zn > Cr > Cu > Mn > Pb > V > Ni > As ~ Sb > Cd > Tl > Pd > Hg > Pt. From the analysis of the temporal variation in the elemental concentration of PM2.5, results show seasonal variations that reach, in general, a maximum in the coldest seasons and a minimum in the warmer seasons. These differences could be explained by the different weather conditions during each season, the influence of the El Niño/La Niña regimen, and the presence of fires on certain sampling dates. The source apportionment analysis performed for the period 2017-2018 showed the contribution to PM2.5 of combustion of heavy fuel oil and diesel-powered vehicles, pet coke, metallurgical and nonferrous industries, paint plant factory, traffic, and natural sources like the soil and road dust. This last analysis completed the assignment of sources for the 10-year period of study. Thus, the results of this work contribute to the implementation of emission reduction strategies in order to decrease the impact of PM2.5 on the environment and the human health.

Accurate total solar irradiance estimates under irradiance measurements scarcity scenarios.

Accurate estimates of total global solar irradiance reaching the Earth's surface are relevant since routine measurements are not always available. This work aimed to determine which of the models used to estimate daily total global solar irradiance (TGSI) is the best model when irradiance measurements are scarce in a given site. A model based on an artificial neural network (ANN) and empirical models based on temperature and sunshine measurements were analyzed and evaluated in Córdoba, Argentina. The performance of the models was benchmarked using different statistical estimators such as the mean bias error (MBE), the mean absolute bias error (MABE), the correlation coefficient (r), the Nash-Sutcliffe equation (NSE), and the statistics t test (t value). The results showed that when enough measurements were available, both the ANN and the empirical models accurately predicted TGSI (with MBE and MABE ≤ |0.11| and ≤ |1.98| kWh m-2 day-1, respectively; NSE ≥ 0.83; r ≥ 0.95; and |t values| < t critical value). However, when few TGSI measurements were available (2, 3, 5, 7, or 10 days per month) only the ANN-based method was accurate (|t value| < t critical value), yielding precise results although only 2 measurements per month were available for 1 year. This model has an important advantage over the empirical models and is very relevant to Argentina due to the scarcity of TGSI measurements.

Latitudinal distribution of OCPs in the open ocean atmosphere between the Argentinian coast and Antarctic Peninsula.

Long-range atmospheric transport is one of the most important ways in which persistent organic pollutants can be transported from their source to remote and pristine regions. Here, we report the results of the first Argentinian measurements of organochlorine pesticides in the Antarctic region. During a 9665-km track onboard OV ARA Puerto Deseado, within the framework of Argentinian Antarctic Expeditions, air samples were taken using high-volume samplers and analyzed using GC-µECD. HCB, HCHs, and endosulfans were the major organic pollutants found, and a north-south gradient in their concentrations was evident by comparing data from the Argentinian offshore zone to the South Scotia Sea.

Field surveys for potential ozone bioindicator plant species in Argentina.

In Argentina no historical or present programs exist specifically assessing ecosystem health with respect to photochemical air pollution, although phytotoxic concentrations of near-ground ozone have been documented in recent years. Here we report our preliminary findings on field observations of ozone-like injury found in natural plant populations and agroecosystems late in the 2005 growing season in the Southern Hemisphere. Several possible ozone bioindicator plants which have not been previously documented were observed to exhibit foliar symptoms consistent with ozone-induced injury. Based on these results we intend to expand field surveys and complete the screening process for injury confirmation of the plant species described here. For this and future research we will be using controlled chamber studies based in the US. Continuous monitoring of tropospheric ozone does not currently take place in the region of central Argentina. The combined evidence provided by intermittent air quality sampling and the presence of ozone-like injury to vegetation indicates the need to establish air quality and ozone biomonitoring networks in this region.

Recovery of the histogram of hourly ozone distribution from weekly average concentrations.

A simple method is presented for estimating hourly distribution of air pollutants, based on data collected by passive sensors on a weekly or bi-weekly basis with no need for previous measurements at a site. In order for this method to be applied to locations where no hourly records are available, reference data from other sites are required to generate calibration histograms. The proposed procedure allows one to obtain the histogram of hourly ozone values during a given week with an error of about 30%, which is good considering the simplicity of this approach. This method can be a valuable tool for sites that lack previous hourly records of pollutant ambient concentrations, where it can be used to verify compliance with regulations or to estimate the AOT40 index with an acceptable degree of exactitude.

A method to estimate emission rates from industrial stacks based on neural networks.

This paper presents a technique based on artificial neural networks (ANN) to estimate pollutant rates of emission from industrial stacks, on the basis of pollutant concentrations measured on the ground. The ANN is trained on data generated by the ISCST3 model, widely accepted for evaluation of dispersion of primary pollutants as a part of an environmental impact study. Simulations using theoretical values and comparison with field data are done, obtaining good results in both cases at predicting emission rates. The application of this technique would allow the local environment authority to control emissions from industrial plants without need of performing direct measurements inside the plant.