Assessing the freshwater quality of a large-scale mining watershed : the need for integrated approaches

Water quality assessments provide essential information for protecting aquatic habitats and stakeholders downstream of mining sites. Moreover, mining companies must comply with environmental quality standards and include public participation in water quality monitoring (WQM) practices. However, overarching challenges beyond corporate environmental responsibility are the scientific soundness, political relevance and harmonization of WQM practices. In this study, a mountainous watershed supporting large-scale gold mining in the headwaters, besides urban and agricultural landuses at lower altitudes, is assessed in the dry season. Conventional physicochemical and biological (Biological Monitoring Water Party-Colombia index) freshwater quality parameters were evaluated, including hydromorphological and land-use characteristics. According to the indicators used, water quality deterioration by mining was absent, in contrast to the effects of urban economic activities, hydromorphological alterations and (less important) agricultural pollutants. We argue that mining impacts are hardly captured due to the limited ecological knowledge of high-mountain freshwaters, including uncharacterized mining-specific bioindicators, environmental baselines and groundwater processes, as well as ecotoxicological and microbial freshwater quality components. Lessons for overcoming scientific and operational challenges are drawn from joint efforts among governments, academia and green economy competitiveness. Facing a rapid development of extractive industries, interinstitutional and multidisciplinary collaborations are urgently needed to implement more integrated freshwater quality indicators of complex mining impacts

Eureka and beyond: mining's impact on African urbanisation

This collection brings separate literatures on mining and urbanisation together at a time when both artisanal and large-scale mining are expanding in many African economies. While much has been written about contestation over land and mineral rights, the impact of mining on settlement, notably its catalytic and fluctuating effects on migration and urban growth, has been largely ignored. African nation-states’ urbanisation trends have shown considerable variation over the past half century. The current surge in ‘new’ mining countries and the slow-down in ‘old’ mining countries are generating some remarkable settlement patterns and welfare outcomes. Presently, the African continent is a laboratory of national mining experiences. This special issue on African mining and urbanisation encompasses a wide cross-section of country case studies: beginning with the historical experiences of mining in Southern Africa (South Africa, Zambia, Zimbabwe), followed by more recent mineralizing trends in comparatively new mineral-producing countries (Tanzania) and an established West African gold producer (Ghana), before turning to the influence of conflict minerals (Angola, the Democratic Republic of Congo and Sierra Leone)

Data mining on urban sound sensor networks

ICA 2016, 22nd International Congress on Acoustics, BUENOS AIRES, ARGENTINE, 05-/09/2016 - 09/09/2016Urban sound sensor networks deliver megabytes of data on a daily basis so the question on how to extract useful knowledge from this overwhelming dataset is eminent. This paper presents and compares two extremely different approaches. The first approach uses as much as possible expert knowledge on how people perceive the sonic environment, the second approach simply considers the spectra obtained every time step as meaningless numbers yet tries to structure them in a meaningful way. The approach based on expert knowledge starts by extracting features that a human listener might use to detect salient sounds and to recognize these sounds. These features are then fed to a recurrent neural network that learns in an unsupervised way to structure and group these features based on co-occurrence and typical sequences. The network is constructed to mimic human auditory processing and includes inhibition and adaptation processes. The outcome of this network is the activation of a set of several hundred neurons. The second approach collects a sequence of one minute of sound spectra (1/8 second time step) and summarizes it using Gaussian mixture models in the frequency-amplitude space. Mean and standard deviation of the set of Gaussians are used for further analysis. In both cases, the outcome is clustered to analyze similarities over space and time as well as to detect outliers. Both approaches are applied on a dataset obtained from 25 measurement nodes during approximately one and a half year in Paris, France. Although the approach based on human listening models is expected to be much more precise when it comes to analyzing and clustering soundscapes, it is also much slower than the blind data analysis

The road to pro-poor growth in Zambia

"Zambia is one of the poorest countries in Sub-Saharan Africa. Almost three-quarters of the population were considered poor at the start of the 1990s, with a vast majority of these people concentrated in rural and remote areas. This extreme poverty arose in spite of Zambia's seemingly promising prospects following independence. To better understand the failure of growth and poverty-reduction this paper first considers the relationship between the structure of growth and Zambia's evolving political economy. A strong urban-bias has shaped the country's growth path leading to an economy both artificially and unsustainably distorted in favor of manufacturing and mining at the expense of rural areas. For agriculture it was the maize-bias of public policies that undermined export and growth potential within this sector....Sustained investment and economic growth during recent years suggest a possible change of fortune for Zambia. In light of this renewed growth, the paper uses a dynamic and spatially-disaggregated economy-wide model linked to a household survey to examine the potential for future poverty-reduction....Although agricultural growth is essential for substantial poverty-reduction, the country's large poor urban population necessitates growth in non-agriculture. The findings suggest that returning to a copper-led growth path is not pro-poor and that non-mining urban growth, although undermined by foreign exchange shortages and inadequate private investment, is likely to be preferable for reducing poverty." Authors' AbstractCopper mines and mining ,Poverty alleviation Africa Zambia ,Manufacturing industries ,Spatial analysis (Statistics) ,Household surveys ,

ECONOMIC EFFECTS OF MINERAL RESOURCE DEVELOPMENT IN NORTHEAST MINNESOTA

The economic effects of mineral resource development addressed in this paper are the changes in employment, population and income in the State of Minnesota and in Northeast Minnesota. These include the present mining, processing and shipping of natural ores and taconite pellets and the potential copper-nickel development.Community/Rural/Urban Development, Resource /Energy Economics and Policy,

ECONOMIC EFFECTS OF COPPER-NICKEL DEVELOPMENT IN NORTHEAST MINNESOTA

Computer simulations of industry gross output, employment and earnings changes associated with alternative copper-nickel development scenarios are presented in this report. The direct and indirect economic effects of seven development scenarios are projected for a mining impact Study Area in St. Louis County, Minnesota.Community/Rural/Urban Development, Resource /Energy Economics and Policy,

Unravelling the relative contributions of climate change and ground disturbance to subsurface temperature perturbations: Case studies from Tyneside, UK

When assessing subsurface urban heat islands (UHIs) it is important to distinguish between localized effects of land-use change and the impacts of global climate change. However, few investigations have successfully unraveled the two influences. We have investigated borehole temperature records from the urban centres of Gateshead and Newcastle upon Tyne in northeast England, to ascertain the effects on subsurface temperatures of climate change and changes in ground conditions due to historic coal mining and more recent urban development. The latter effects are shown to be substantial, albeit with significant variations on a very local scale. Significant subsurface UHIs are indeed evident in both urban centres, estimated as 2.0 °C in Newcastle and 4.5 °C in Gateshead, the former value being comparable to the 1.9 °C atmospheric UHI previously measured for the Tyneside conurbation as a whole. We interpret these substantial subsurface UHIs as a consequence of the region’s long history of urban and industrial development and associated surface energy use, possibly supplemented in Gateshead by the thermal effect of trains braking in an adjacent shallow railway tunnel. We also show that a large proportion of the expected conductive heat flux from the Earth’s interior beneath both Gateshead and Newcastle becomes entrained by groundwater flow and transported elsewhere, through former mineworkings in which the rocks have become ‘permeabilised’ during the region’s long history of coal mining. Discharge of groundwater at a nearby minewater pumping station, Kibblesworth, has a heat flux that we estimate as ∼7.5 MW; it thus ‘captures’ the equivalent of roughly two thirds of the geothermal heat flux through a >100 km2 surrounding region. Modelling of the associated groundwater flow regime provides first-order estimates of the hydraulic transport properties of ‘permeabilised’ Carboniferous Coal Measures rocks, comprising permeability ∼3 × 10−11 m2 or ∼30 darcies, hydraulic conductivity ∼2 × 10−4 m s−1, and transmissivity ∼2 × 10−3 m2 s−1 or ∼200 m2 day−1; these are very high values, comparable to what one might expect for karstified Carboniferous limestone. Furthermore, the large-magnitude subsurface UHIs create significant downward components of conductive heat flow in the shallow subsurface, which are supplemented by downward heat transport by groundwater movement towards the flow network through the former mineworkings. The warm water in these workings has thus been heated, in part, by heat drawn from the shallow subsurface, as well as by heat flowing from the Earth’s interior. Similar conductive heat flow and groundwater flow responses are expected in other urban former coalfield regions of Britain; knowledge of the processes involved may facilitate their use as heat stores and may also contribute to UHI mitigation

A comparison of classification techniques for monitoring and mapping land cover and land use changes in the subtropical region of Thai Nguyen, Vietnam : a thesis presented in partial fulfilment of the requirements for the degree of Master of Environmental Management at Massey University, Palmerston North, New Zealand

Deriving land cover/land-use information from earth observation satellite data is one of the most common applications for environmental monitoring, evaluation and management. Many parametric and non-parametric classification algorithms have been developed and applied to such applications. This study looks at the classification accuracies of three algorithms for different spatial and spectral resolution data. The performance of Random Forest (RF) was compared to Maximum Likelihood (MLC) and Artificial Neural Network (ANN) algorithms for the separation of subtropical land cover/land-use categories using Sentinel-2 and Landsat 8 data. The overall, producers’ and users’ accuracies were derived from the confusion matrix, while local land use statistics were also collected to evaluate the accuracy of classified images. The accuracy assessment showed the RF algorithm regularly outperformed the MLC and ANN in both types of imagery data (>90%). This approach also exhibited potential in dealing with the challenge of separating similar man-made features such as urban/built-up and mining extraction classes. The ANN algorithm had the lowest accuracy among the three classification algorithms, while Landsat 8 imagery was most suitable for the classification of subtropical mixed and complex landscapes. As the RF algorithm demonstrated a robustness and potential for mapping subtropical land cover/land-use, this study chose it to monitor and map temporal land cover/land-use changes in Thai Nguyen, Vietnam between 2000 and 2016. The results of this temporal monitoring revealed that there were substantial changes in land cover/land use over the course of 16 years. Agricultural and forest land decreased, while urban and mining extraction land expanded significantly, and water increased slightly. Changes in land cover/land-use are strongly associated with geographic locations. The conversion of agriculture and forest into urban/builtup and mining extraction land was detected largely in the Thai Nguyen central city and southern regions. In addition, further GIS analysis revealed that approximately 69.6% (100.2km2) of new built-up areas had occurred within 2km of primary roads, and nearly 96% (137.6km2) of new built-up expansion was detected within a 5-km buffer of the main roads. This study also demonstrates the potential of multi-temporal Landsat data and the combination of remote sensing, GIS and R programming to provide a timely, accurate and economical means to map and analyse temporal changes for long-term local land use development planning. Keywords: Random forest; Land cover mapping; Remote Sensing; Vietna