Using Spatial Pattern to Quantify Relationship Between Samples, Surroundings, and Populations

The need for accurate carbon budgeting, climate change modelling, and sustainable resource management has lead to an increase in the number of large area forest monitoring programs. Large area forest monitoring programs often utilize field and remotely sensed data sources. Sampling, via field or photo plots, enables the collection of data with the desired level of categorical detail in a timely and efficient manner. When sampling, the aim is to collect representative detailed data enabling the statistical reporting upon the characteristics of larger areas. As a consequence, approaches for investigating how well sample data represent larger areas (i.e., the sample neighbourhood and the population) are desired. Presented in this communication is a quantitative approach for assessing the nature of sampled areas in relation to surrounding areas and the overall population of interest. Classified Landsat data is converted to forest/non-forest categories to provide a consistent and uniform data set over a 130,000 km² study region in central British Columbia, Canada. From this larger study area 322 2 × 2 km photo plots on a 20 × 20 km systematic grid are populated with composition and configuration information for comparison to non-sampled areas. Results indicate that typically, within the study area, the spatial pattern of forest within a photo plot is representative of the forest patterns found within primary and secondary neighbourhoods and over the entire population of the study. These methods have implications for understanding the nature of data used in monitoring programs worldwide. The ability to audit photo and field plot information promotes an increased understanding of the results developed from sampling and provides tools identifying locations of possible bias.     

Application of a “closed-can” technique for measuring radon exhalation from mine samples of Punjab, Pakistan

Using the closed-can technique, radon exhalation rate measurements have been carried out for shale and coal samples collected from various mines located in the Chakwal and Makarwal areas of Pakistan. For the two areas, the measured average values of the exhalation rates from shale are 1.45±0.13 and 0.67±0.25 Bq m⁻² h⁻¹ and for coal are 1.0±0.03 and 0.65±0.32 Bq m⁻² h⁻¹, respectively. These values are much lower than the measured exhalation rates from alum-shale-based Nordic concrete which has values in the 50–200 Bq m⁻² h⁻¹ range. The lower values of the measured exhalation rates from the shale and coal deposits in the Chakwal and Makarwal areas are indicative of their lower uranium contents and mine workers in these areas do not face any abnormal health hazard due to radon since the exhalation rates have been found to be on the low side.     

Fine-resolution mapping of wildfire fuel types for Canada: Fuzzy logic modeling for an Alberta pilot area

The Canadian Forest Fire Danger Rating System (CFFDRS) is used daily across Canada for evaluating forest fire danger. Fuel-type information is one of the inputs required by the models used in the CFFDRS. In this project, three fuel-type maps with a 25 m resolution were produced for a pilot study area located in Alberta using land cover only; land cover and biomass; and, land cover, biomass and leaf area index data derived from satellite imagery. The relationships between inputs and fuel types were determined mainly by the opinions of forest fire scientists and incorporated into a computer program using fuzzy set methodology. Not all the CFFDRS fuel types could be distinguished using these inputs; three of the coniferous types had to be grouped into one common fuel type. Overall accuracy was between 74 and 83% based on ground-truth comparisons. The most accurate map resulted from land cover and biomass data. Detailed accuracy assessment indicated that the overall accuracy increased up to 86% if ambiguous fuel type identification was considered. No combination of inputs was able to define a fuel type with absolute certainty, which is a reflection of differing expert opinions and the small number of inputs used to produce the maps.     

Spatial Characterization and Prioritization of Heavy Metal Contaminated Soil-Water Resources in Peri-Urban Areas of National Capital Territory (NCT), Delhi

Due to rapid industrialization and urbanization during last two decades, contamination of soils by heavy metals is on an increase globally. Lands under peri-urban agriculture are the worst affected. In NCT, Delhi about 14.4% of land area is chemically degraded. In order to take care of this problem, recently the Supreme Court of India ordered to shift various non-confirming (about 39,000 units) industries to regions outside NCT, Delhi. However in spite of this, there have been several reports and parliamentary debates on the phyto-toxicity and extensive accumulation of heavy metals in the region. Literature review revealed that the basis of these debates is a few studies on some point locations in/around Delhi. It was further observed that information on the distribution and extent of heavy metal pollution problem in the region was completely missing. The present study was thus basically aimed at assessing the spatial distribution/extent and type of heavy metal pollution in the study area, for enabling future designing of appropriate site-specific management measures by the decision makers.For this, detailed spatial information on bio-available heavy metal concentrations in the soils and surface/sub-surface waters of NCT (Delhi) was generated through actual soil/water surveys, standard laboratory methods and GIS techniques. The study showed that concentration of all micronutrients (viz. Zn: 0.05–0.18 ppm; Cu: in traces; Fe: 0–0.5 ppm; and Mn: 0–1.2 ppm) and most heavy metals (viz. Ni: 0–0.7 ppm; Pb: 0–0.15 ppm and Cd: in traces) in the surface/sub-surface irrigation waters were well within permissible limits. However Cr concentrations in irrigation waters of Alipur and Shahdara blocks were far above their maximum permissible limit of 1 ppm. It was further observed that Ni and Cr concentrations in the drinking waters of almost entire test area were far above maximum permissible levels of 0.02 and 0.01 ppm, respectively. Bio-available concentrations of several heavy metals (viz. Pb: 0.1–2 ppm; Cd: traces; Ni: 0.05–2 ppm and Cr: 0–0.4 ppm) in the study area soils were also observed to be well within the maximum permissible limits. However there were point Cu contaminations (5–10 ppm) in the sewage-sludge amended soils of vegetable growing areas near south Shahdara block. This was attributed to increased Cu availability due to oxidized acidic conditions generated by over-irrigation of agricultural lands. Available Mn concentrations in Kanjhawala, western Najafgarh and Alipur soils were also observed to be above maximum permissible limit of 10 ppm. This was observed to be mainly due to the geology (i.e. presence of Mn rich sedimentary rocks) and prevalence of reduced acidic conditions, due to paddy cultivation, in these areas. It was further observed that there is acute zinc (Zn) deficiency (< 0.6 ppm) in paddy growing soils of north Kanjhawala, Alipur and some parts of Najafgarh and Shahdara blocks due to extensive leaching of available Zn fractions to lower soil horizons. Similar available Zn deficiencies in high pH (8.5) soils of areas around Bamnoli village in E-Najafgarh block were also observed.     

Near Real-Time Monitoring and Mapping of Specific Conductivity Levels Across Lake Texoma, USA

A submersible sonde equipped with a specific conductivity probe, linked with a global positioning satellite receiver was developed, deployed on a small boat, and used to map spatial and temporal variations in specific conductivity in a large reservoir. 7,695 sample points were recorded during 8 sampling trips. Specific conductivity ranged from 442,uS/cm to 3,378,uS/cm over the nine-month study. The data showed five statistically different zones in the reservoir: 2 different riverine zones, 2 different riverine transition zones, and a lacustrine zone (the main lake zone). These data were imported to a geographic information system where they were spatially interpolated to generate 8 maps showing specific conductivity levels across the entire surface of the lake. The highly dynamic nature of water quality, due to the widely differing nature of the rivers that flow into the reservoir and the effect of large inflows of fresh water during winter storms is easily captured and visualized using this approach.     

Inferring coastal processes from regional-scale mapping of Radon and salinity: examples from the Great Barrier Reef, Australia

The radon isotope ²²²Rn and salinity in coastal surface water were mapped on regional scales, to improve the understanding of coastal processes and their spatial variability. Radon was measured with a surface-towed, continuously recording multi-detector setup on a moving vessel. Numerous processes and locations of land-ocean interaction along the Central Great Barrier Reef coastline were identified and interpreted based on the data collected. These included riverine fluxes, terrestrially-derived fresh submarine groundwater discharge (SGD) and the tidal pumping of seawater through mangrove forests. Based on variations in the relationship of the tracers radon and salinity, some aspects of regional freshwater inputs to the coastal zone and to estuaries could be assessed. Concurrent mapping of radon and salinity allowed an efficient qualitative assessment of land-ocean interaction on various spatial and temporal scales, indicating that such surveys on coastal scales can be a useful tool to obtain an overview of SGD locations and processes.     

Coupled radon, methane and nitrate sensors for large-scale assessment of groundwater discharge and non-point source pollution to coastal waters

We constructed a survey system of radon/methane/nitrate/salinity to find sites of submarine groundwater discharge (SGD) and groundwater nitrate input. We deployed the system in Waquoit Bay and Boston Harbor, MA where we derived SGD rates using a mass balance of radon with methane serving as a fine resolution qualitative indicator of groundwater. In Waquoit Bay we identified several locations of enhanced groundwater discharge, out of which two (Childs and Quashnet Rivers) were studied in more detail. The Childs River was characterized by high nitrate input via groundwater discharge, while the Quashnet River SGD was notable but not a significant source of nitrate. Our radon survey of Boston Harbor revealed several sites with significant SGD, out of these Inner Harbor and parts of Dorchester Bay and Quincy Bay had groundwater fluxes accompanied by significant water column nitrogen concentrations. The survey system has proven effective in revealing areas of SGD and non-point source pollution.     

A Monte Carlo study of radon detection in cylindrical diffusion chambers

The functioning of radon diffusion chambers was studied using the Monte Carlo code RAMMX developed here. The alpha particles from radon are assumed randomly produced in the volume of the cylinder, and those from the progeny are assumed to originate randomly at the cylindrical surface. The energy spectrum, the distribution of incident angles, and the distribution of path lengths of the alpha particles on the detector were obtained. These quantities vary depending on input parameters such as initial alpha particle energy, radius and depth of the diffusion chamber, detector size and atmospheric pressure. The calculated energy spectrum for both ²²²Rn and ²²⁰Rn was compared with experiment, permitting the identification of each peak and its origin, and a better understanding of radon monitoring. Three aspects not considered in previous calculations are progeny alphas coming from surfaces of the monitor, taking into account the atmospheric pressure, and including the isotope ²²⁰Rn.     

Radon, helium and CO2 measurements in soils overlying a former exploited oilfield, Pechelbronn district, Bas-Rhin, France

The Pechelbronn oilfield (Rhine Graben, France), where mining activity ended in the 1960s, has been used for waste disposal for twenty years. Since the wastes are varied, work is underway to identify the discharged materials and their derivatives, as well as to locate and quantify potential discharge sites. Two major goals were assigned to the present work. The first was to identify or refine the location of hidden structures that could facilitate gas emanation up to the surface, by studying soil gas concentrations (mainly ²²²Rn, CO₂, CH₄ and helium) and carbon isotope ratios in the CO₂ phase. The second was devoted to examining, from a health and safety viewpoint, if the use of the oilfield as a waste disposal site might have led to enhanced or modified gas emanation throughout the area.It appeared that CO₂ and ²²²Rn evolution in the whole area were similar, except near some of the faults and fractures that are known through surface mapping and underground observations. These ²²²Rn and CO₂ anomalies made it possible to highlight more emissive zones that are either related to main faults or to secondary fractures acting as migration pathways. In that sense, the CO₂ phase can be used to evaluate ²²²Rn activities distant from tectonic structures but can lead to erroneous evaluations near to gas migration pathways. Dumping of wastes, as well as oil residues, did not appear to have a strong influence on soil gaseous species and emanation. Similarly, enhanced gas migration due to underground galleries and exploitation wells has not been established. Carbon isotope ratios suggested a balance of biological phenomena, despite the high CO₂ contents reached. Other monitored gaseous species (N₂, Ar, H₂ and alkanes), when detected, always showed amounts close to those found subsurface and/or in atmospheric gases.     

Inverse modeling of Asian Rn flux using surface air Rn concentration

When used with an atmospheric transport model, the ²²²Rn flux distribution estimated in our previous study using soil transport theory caused underestimation of atmospheric ²²²Rn concentrations as compared with measurements in East Asia. In this study, we applied a Bayesian synthesis inverse method to produce revised estimates of the annual ²²²Rn flux density in Asia by using atmospheric ²²²Rn concentrations measured at seven sites in East Asia. The Bayesian synthesis inverse method requires a prior estimate of the flux distribution and its uncertainties. The atmospheric transport model MM5/HIRAT and our previous estimate of the ²²²Rn flux distribution as the prior value were used to generate new flux estimates for the eastern half of the Eurasian continent dividing into 10 regions.The ²²²Rn flux densities estimated using the Bayesian inversion technique were generally higher than the prior flux densities. The area-weighted average ²²²Rn flux density for Asia was estimated to be 33.0 mBq m⁻² s⁻¹, which is substantially higher than the prior value (16.7 mBq m⁻² s⁻¹). The estimated ²²²Rn flux densities decrease with increasing latitude as follows: Southeast Asia (36.7 mBq m⁻² s⁻¹); East Asia (28.6 mBq m⁻² s⁻¹) including China, Korean Peninsula and Japan; and Siberia (14.1 mBq m⁻² s⁻¹). Increase of the newly estimated fluxes in Southeast Asia, China, Japan, and the southern part of Eastern Siberia from the prior ones contributed most significantly to improved agreement of the model-calculated concentrations with the atmospheric measurements. The sensitivity analysis of prior flux errors and effects of locally exhaled ²²²Rn showed that the estimated fluxes in Northern and Central China, Korea, Japan, and the southern part of Eastern Siberia were robust, but that in Central Asia had a large uncertainty.