LEAD ISOTOPIC RATIOS IN ENVIRONMENTAL SAMPLES AS ORIGIN MARKERS Presenter: María Trinidad Martínez Castillo Authors: Alberto FERNÁNDEZ, Trinidad MARTÍNEZ, Graciela ZARAZUA, Samuel TEJEDA, Pedro AVILA- PÉREZ
Introduction: Environmental pollution due to heavy metals is known since antiquity. Human contact with non- natural amounts of heavy metals increases with industrial development. In Mexico, about three millions people die every single year because of air pollution, a figure amounting 5% of total deaths in the country
The Mexico City s gray side
Lead, well known pollutant The U. S. Environmental Protection Agency (EPA) classifies lead as a very toxic element, widely distributed on the crustal through natural ways. According to EPA, the use of fossil fuels, leaded gasoline, lead paints and industrial activity count as the main lead- pollutant sources. Tetraethylead (antiknoct)
Lead emissions in Metropolitan Zone of Toluca Valley Toluca s downtown, in the back the volcano Nevado de Toluca
Low- lead and unleaded gas, and a stricter regulation of car emissions were introduced in 1996 in Mexico. That year, motor- vehicles lead emissions was about 97 tons per year in the Metropolitan Zone of Toluca Valley (MZTV). Mexico did rank the fifth world position as lead mine producer (126 kt) and eighth as a primary and secondary lead producer (147 kt and 105 kt) in 2005.
Sampling sites Site Clas. Description Site Clas. Description 1 Negrete UA Park in the surrounding of Toluca 9 Acazulco TA Group of tree in rural zone with agriculture influence 2 Alameda UA Urban Park 10 El Pedregal TA 3 Reforma UA Urban Park 11 San Miguel TA 4 Hípico UA Urban Park 12 Ameyalco TA 5 La Pila UA Urban Park 13 San Antonio TA Group of tree in rural zone with agriculture influence Group of tree in rural zone with agriculture influence Group of tree in rural zone with agriculture influence Group of tree in rural zone with agriculture influence 6 Santín UA Edge of tree in a high way 14 San Diego NPA Protected Forest. 7 Tollocan UA Edge of tree into an industrial park. 15 Cacalomacán NPA Protected Forest. 8 Lomas Altas NPA Natural Park into the city. 16 Ciervita NPA Protected Forest.
Environmental samples Mosses (dry- cold season and humid- warm season) On- surface soils Street dust Aerosols*
Possible lead sources Natural process (Drinkable water) Industrial process (lead pellets from lead- smelting) Fossil fuels (Magna, Premium and Diesel) Rural activities (Coal)
Experimental The methods for collecting and processing samples were taken from Zarazua et. al 2013, Tejeda et. al 2013 and C. L. Sudgen 1993. All re- agents were ultra- high pure and special for trace analyses. The digested samples were measured in ICP- Ms equipment with quadrupole. The relative abundances for 4 stables isotopes of lead (204Pb, 206Pb, 207Pb and 208Pb) were obtained.
The six possible isotopic ratios among four stable isotopes of lead were calculated and normality proofs, atypical data study, homoscedastic proofs, T test, and ANOVAs, using Statgraphics software, were applied on them.
Results and discussions Possible lead sources All samples of drinkable water do not have significant differences between them for each one of isotopic rates. Industrial lead (small pellets) has differences with the others lead sources We separate the fossil fuel into two groups (Gasoline and Diesel) because significant differences shown in ANOVA
Some authors have proposed to calculate sample pollutant shares by equation systems. They propose the linear combinations of isotopic rates on lead sources in order to explain isotopic rates on samples. Linear- combination coefficients for each source represent the contribution percentage for samples. We decided not to use this method because lack of linear independence between equations that contain the same isotope, for instance 206 Pb/ 204 Pb and 207 Pb/ 204 Pb. Additionally, if we calculated the contribution percentage from one specific lead source with inverse isotopic rates ( 206 Pb/ 207 Pb and 207 Pb/ 206 Pb) we would obtain different results with each one.
Cluster analysis A cluster analysis was carried out to each kind of environmental sample adding the five possible lead sources in the analysis. The biggest clusters in the analysis realized to moss, soils and street dust were with drinkable water and lead pellets. Meanwhile gasoline has the biggest one in the case of Aerosols.
Distance We calculated the Euclidean distance between environmental sample and each possible lead source. After that we selected the shortest distance for every single environmental sample. This study has an advantage comparing to cluster analysis because we are able to make a difference between environmental samples and possible lead sources. The lead sources being closer to the environmental samples are: drinkable water and lead pellets. Gasoline ranked third
Environmental Samples There are many significant differences between two sampling season of moss. Moss samples of the humid- warm season show fewer significant differences between areas with same classification. The soil samples have a greater significant differences with dry- cold moss. Street dust from busy places is closer to natural lead, in the same way street dust from quiet places with industrial lead. Aerosols did show to be closer to gasoline than other environmental samples.
Conclusions MZVT main pollutant sources are the following: lead from natural processes and industrial lead for soil, moss and street dust samples. Motor car emissions exert a big influence on aerosol lead distribution. Rain fall and slow wind favor homogeneity among soil and moss, while strong winds and scarce rain fall favor particles bigger running over before being deposited. For that case, obtained data through statistical analysis (ANOVA and conglomerate analysis) and distance calculation seems to be more acute than those obtained by algebraic methods.
Our future aim is to find a procedure to calculate average share per source in a more precise way. The standard recommendation is to use an additional variable to absorb errors coming from linear dependence of equations involving an even isotope. Linear programming to optimize coefficient values of linear combinations has also been practiced. A third way is to calculate the average shares according to linear combinations of each isotope abundance in both samples and sources.
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