Héctor Jorquera

Forecasting ozone daily maximum levels at Santiago, Chile

Abstract In major urban areas, air pollution impact on health is serious enough to include it in the group of meteorological variables that are forecast daily. This work focusses on the comparison of different forecasting systems for daily maximum ozone levels at Santiago, Chile. The modelling tools used for these systems were linear time series, artificial neural networks and fuzzy models. The structure of the forecasting model was derived from basic principles and it includes a combination of persistence and daily maximum air temperature as input variables. Assessment of the models is based on two indices: their ability to forecast well an episode, and their tendency to forecast an episode that did not occur at the end (a false positive). All the models tried in this work showed good forecasting performance, with 70–95% of successful forecasts at two monitor sites: Downtown (moderate impacts) and Eastern (downwind, highest impacts). The number of false positives was not negligible, but this may be improved by expressing the forecast in broad classes: low, average, high, very high impacts; the fuzzy model was the most reliable forecast, with the lowest number of false positives among the different models evaluated. The quality of the results and the dynamics of ozone formation suggest the use of a forecast to warn people about excessive exposure during episodic days at Santiago.

An intervention analysis of air quality data at Santiago, Chile

Abstract Air quality data at Santiago, Chile (PM10, PM2.5 and ozone) from 1989 to 1998 are analyzed with the goal of estimating trends in and impacts of public policies on air quality levels. Those policies, in effect since the late 1980s, have been essentially aimed at PM10 pollution abatement. The analyses show that fall and winter air quality has been improving consistently, specially the PM2.5 levels. The estimated trends for the monthly averages of PM10 concentrations range from −1.5 to −3.3% per annum, whereas the trends for monthly averages of PM2.5 concentrations range from −5 to −7% per annum. The monthly averages of ground ozone daily maxima do not have a significant trend for two of the downtown monitor sites; at the other three monitoring sites (including the one with the highest impacts) there is a clear downward trend between −5 and −3% per annum. The seasonal averages of a declimatized ozone production rate show a downward trend from 1988 through 1995, and no additional improvements have occurred thereafter. These mixed results for ground ozone levels are ascribed to a shift in the magnitude and spatial distribution of emissions in the city, and so there is a need for additional ozone abatement policies and further research on air pollution abatement options.

Air quality at Santiago, Chile: a box modeling approach—I. Carbon monoxide, nitrogen oxides and sulfur dioxide

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The period analyzed is 1990–2000, characterized by the introduction of air pollution emissions standards, shift to unleaded gasoline and compressed natural gas, and steady growth of the private and public fleet and the associated fuel consumption growth. The box models explicitly include the seasonal behavior of meteorological variables; the results show that dispersion conditions in fall and winter seasons are 20–30% of the summertime values. This result explains the poor air quality in those seasons and shows that significant emissions reductions are required in order to improve air quality in wintertime. Emissions of CO, NOx and SO2 are estimated from data on fuel consumption in the city; the estimated parameters are thus fleet-average or industry-average emission factors. In terms of contributions to ambient concentrations, older cars and diesel vehicles are the major contributors to CO and NOx impacts, with more than 60% and 50%, respectively. Ambient concentrations of SO2 are largely dominated by stationary sources, although long range contributions are not negligible. By contrast, CO and NOx pollution is dominated by local sources within the city boundaries. The box models can be used for forecasting purposes, and they can predict annual average concentrations within 20% of the observed values. The methodology requires data on ambient air quality measurements and fuel consumption statistics, and produces quantitative results, which can be combined with economic models to analyze environmental regulation and public policies.

Chemical speciation and source apportionment of fine particulate matter in Santiago, Chile, 2013

Santiago is one of the largest cities in South America and has experienced high fine particulate matter (PM2.5) concentrations in fall and winter months for decades. To better understand the sources of fall and wintertime pollution in Santiago, PM2.5 samples were collected for 24 h every weekday from March to October 2013 for chemical analysis. Samples were analyzed for mass, elemental carbon (EC), organic carbon (OC), water soluble organic carbon (WSOC), water soluble nitrogen (WSTN), secondary inorganic ions, and particle-phase organic tracers for source apportionment. Selected samples were analyzed as monthly composites for organic tracers. PM2.5 concentrations were considerably higher in the coldest months (June-July), averaging (mean ± standard deviation) 62±15 μg/m(3) in these two months. Average fine particle mass concentration during the study period was 40±20 μg/m(3). Organic matter during the peak winter months was the major component of fine particles comprising around 70% of the particle mass. Source contributions to OC were calculated using organic molecular markers and a chemical mass balance (CMB) receptor model. The four combustion sources identified were wood smoke, diesel engine emission, gasoline vehicles, and natural gas. Wood smoke was the predominant source of OC, accounting for 58±42% of OC in fall and winter. Wood smoke and nitrate were the major contributors to PM2.5. In fall and winter, wood smoke accounted for 9.8±7.1 μg/m(3) (21±15%) and nitrate accounted for 9.1±4.8 μg/m(3) (20±10%) of fine PM. The sum of secondary inorganic ions (sulfate, nitrate, and ammonium) represented about 30% of PM2.5 mass. Secondary organic aerosols contributed only in warm months, accounting for about 30% of fine PM during this time.

Source apportionment of ambient PM2.5 in Santiago, Chile: 1999 and 2004 results.

A receptor model analysis has been applied to ambient PM(2.5) measurements taken at Santiago, Chile (33.5°S, 70.7°W) in 2004 (117 samples) and in 1999 (95 samples) on a receptor site on the eastern side of the city. For both campaigns, six sources have been identified at Santiago and their contributions in 1999/2004 are: motor vehicles: 28 ± 2.5/31.2 ± 3.4%, wood burning: 24.8 ± 2.3/28.9 ± 3.3%, sulfates: 18.8 ± 1.7/16.2 ± 2.5%, marine aerosol: 13 ± 2.1/9.9 ± 1.5%, copper smelters: 11.5 ± 1.4/9.7 ± 3.3% and soil dust: 3.9 ± 1.5/4.0 ± 2.4%. Hence relative contributions are statistically the same but the absolute contributions have been reduced because ambient PM(2.5) has decreased from 34.2 to 25.1 μg/m(3) between 1999 and 2004 at Santiago. Similarity of results for both data sets - analyzed with different techniques at different laboratory facilities - shows that the analysis performed here is robust. Source identification was carried out by inspection of key species in source profiles, seasonality of source contributions, comparison with published source profiles and by looking at wind trajectories computed using the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) from USAs National Oceanic and Atmospheric Administration (NOAA); for the wood burning sources the MODIS burned area daily product was used to confirm wildfire events along the year. Using this combined methodology we have shown conclusively that: a) marine air masses do reach Santiagos basin in significant amounts but combined with anthropogenic sources; b) all copper smelters surrounding Santiago - and perhaps coal-fired power plants as well - contribute to ambient PM(2.5); c) wood burning is the second largest source, coming from residential wood burning in fall and winter and from regional wildfires in spring and summer. The results of the present analysis can be used to improve emission inventories, air quality forecasting systems and cost-benefit analyses at local and regional scales.

Air quality at Santiago, Chile: a box modeling approach II. PM2.5, coarse and PM10 particulate matter fractions

Abstract Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM10, PM2.5 and coarse (PM10–PM2.5) particulate matter (PM) fractions; the period analyzed is 1989–1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO2 concentrations, plus a term proportional to (wind speed)−1 that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO2 coefficients fitted for 1989–1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM2.5 and PM10 have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology.

Macroscopic growth of filamentous fungi on solid substrate explained by a microscopic approach

A quantitative model predicting biomass growth on solid media has been developed. The model takes into account steric interactions between hyphae and tips at the microscopic level (competition for substrate and tip-hypha collisions). These interactions effect a slowing down of the hyphal, population-averaged extension rate and are responsible, at the microscopic level, for the distribution of tip orientations observed at the colony border. At the macroscopic level, a limiting value of the colony radial extension rate is attained. A mathematical model that combines hyphal branching, tip diffusion, and biomass growth was proposed to explain such behavior. Experiments using Gibberella fujikuroi were performed to validate the model; good agreement between experiments and simulations was achieved. Most parameters can be measured by simple image analysis on the peripheral growth zone, and they have clear physical meaning; that is, they correspond to properties of single, leading hyphae. The model can be used to describe two-dimensional (2D) solid media fermentation experiments under varying culture conditions; the model can also be extended to consider growth in three-dimensional (3D), complex geometry substrates.

Source apportionment of PM10 and PM2.5 in a desert region in northern Chile

Estimating contributions of anthropogenic sources to ambient particulate matter (PM) in desert regions is a challenging issue because wind erosion contributions are ubiquitous, significant and difficult to quantify by using source-oriented, dispersion models. A receptor modeling analysis has been applied to ambient PM(10) and PM(2.5) measured in an industrial zone ~20 km SE of Antofagasta (23.63°S, 70.39°W), a midsize coastal city in northern Chile; the monitoring site is within a desert region that extends from northern Chile to southern Perú. Integrated 24-hour ambient samples of PM(10) and PM(2.5) were taken with Harvard Impactors; samples were analyzed by X Ray Fluorescence, ionic chromatography (NO(3)(-) and SO(4)(=)), atomic absorption (Na(+), K(+)) and thermal optical transmission for elemental and organic carbon determination. Receptor modeling was carried out using Positive Matrix Factorization (US EPA Version 3.0); sources were identified by looking at specific tracers, tracer ratios, local winds and wind trajectories computed from NOAAs HYSPLIT model. For the PM(2.5) fraction, six contributions were found - cement plant, 33.7 ± 1.3%; soil dust, 22.4 ± 1.6%; sulfates, 17.8 ± 1.7%; mineral stockpiles and brine plant, 12.4 ± 1.2%; Antofagasta, 8.5 ± 1.3% and copper smelter, 5.3 ± 0.8%. For the PM(10) fraction five sources were identified - cement plant, 38.2 ± 1.5%; soil dust, 31.2 ± 2.3%; mineral stockpiles and brine plant, 12.7 ± 1.7%; copper smelter, 11.5 ± 1.6% and marine aerosol, 6.5 ± 2.4%. Therefore local sources contribute to ambient PM concentrations more than distant sources (Antofagasta, marine aerosol) do. Soil dust is enriched with deposition of marine aerosol and calcium, sulfates and heavy metals from surrounding industrial activities. The mean contribution of suspended soil dust to PM(10) is 50 μg/m(3) and the peak daily value is 104 μg/m(3). For the PM(2.5) fraction, suspended soil dust contributes with an average of 9.3 μg/m(3) and a peak daily value of 31.5 μg/m(3).

Source apportionment of PM10 and PM2.5 at Tocopilla, Chile (22°05’S, 70°12’W)

Tocopilla is located on the coast of Northern Chile, within an arid region that extends from 30° S to the border with Perú. The major industrial activities are related to the copper mining industry. A measurement campaign was conducted during March and April 2006 to determine ambient PM10 and PM2.5 concentrations in the city. The results showed significantly higher PM10 concentrations in the southern part of the city (117 μg/m3) compared with 79 and 80 (μg/m3) in the central and northern sites. By contrast, ambient PM2.5 concentrations had a more uniform spatial distribution across the city, around 20 (μg/m3). In order to conduct a source apportionment, daily PM10 and PM2.5 samples were analyzed for elements by XRF. EPA’s Positive Matrix Factorization software was used to interpret the results of the chemical compositions. The major source contributing to PM2.5 at sites 1, 2 and 3, respectively are: (a) sulfates, with ˜50% of PM2.5 concentrations at the three sites; (b) fugitive emissions from fertilizer storage and handling, with 16%, 21% and 10%; (c) Coal and residual oil combustion, with 15%, 15% and 4%; (d) Sea salt, 5%, 6% and 16%; (e) Copper ore processing, 4%, 5% and 15%; and (f) a mixed dust source with 11%, 7% and 4%. Results for PM10—at sites 1, 2 and 3, respectively—show that the major contributors are: (a) sea salt source with 36%, 32% and 36% of the PM10 concentration; (b) copper processing emissions mixed with airborne soil dust with 6.6%, 11.5% and 41%; (c) sulfates with 31%, 31% and 12%; (d) a mixed dust source with 16%, 12% and 10%, and (e) the fertilizer stockpile emissions, with 11%, 14% and 2% of the PM10 concentration. The high natural background of PM10 implies that major reductions in anthropogenic emissions of PM10 and SO2 would be required to attain ambient air quality standards for PM10; those reductions would curb down ambient PM2.5 concentrations as well.

Data analysis using regression models with missing observations and long-memory: an application study

The objective of this work is to propose a statistical methodology to handle regression data exhibiting long memory errors and missing values. This type of data appears very often in many areas, including hydrology and environmental sciences, among others. A generalized linear model is proposed to deal with this problem and an estimation strategy is developed that combines both classical and Bayesian approaches. The estimation methodology proposed is illustrated with an application to air pollution data which shows the impact of the long memory in the statistical inference and of the missing values on the computations. From a Bayesian standpoint, genuine priors are considered for the parameters of the model which are justified within the context of the air pollution model derivation.