Adapting bridge infrastructure to climate change: institutionalizing resilience in intergovernmental transportation planning processes in the Northeastern USA

Multi-level governance networks provide both opportunities and challenges to mainstream climate change adaptation due to their routine decision-making and coordination processes. This paper explores institutionalizing resilience and adaptation to climate change in the intergovernmental transportation planning processes that address bridge infrastructure in the Northeastern United States (USA), specifically in Vermont and Maine. The research presented here relies on nine interviews with policy-makers and planners, a survey of transportation project prioritization criteria, development of a longitudinal bridge funding database, and its integration with publicly available geospatial data. It presents a novel spatial analysis methodology, a modified version of which could be adopted by transportation agencies for prioritizing scarce adaptation funds. Although transportation agencies are undertaking a variety of mitigation activities to address business-as-usual needs, climate change adaptation and resilience efforts remain underprioritized. Adaptation is a global concern, but impacts vary dramatically between regions and require localized solutions. Bridges and culverts, which are especially vulnerable to climate-induced flooding impacts, have complex maintenance and design processes and are subject to convoluted adaptation planning procedures. Critical gaps in resources and knowledge are barriers to improved adaptation planning. Restructuring the transportation project prioritization procedures used by planning organizations to explicitly include adaptation may provide a novel strategy to institutionalize resilience in transportation. These procedures must be considered in the context of the intergovernmental networks that exist to support transportation infrastructure. Although these networks will likely vary across countries, the approaches introduced here to study and address transportation infrastructure adaptation may be applied to many settings.     

Dynamic interactive links among sustainable energy investment,air pollution,and sustainable development in regional China

Environmental Science and Pollution Research - This research investigates the dynamic interactive associations among sustainable investment in the energy sector, air pollution, and sustainable...     

Effects of population structure on CO2 emissions in South Asian countries: evidence from panel estimation

Environmental Science and Pollution Research - The rapidly changing population structure in South Asian countries is observed to be linked with growing social, economic, and environmental problems....     

Nexus between green logistic operations and triple bottom line: evidence from infrastructure-led Chinese outward foreign direct investment in Belt and Road host countries

Environmental Science and Pollution Research - The Belt and Road initiative (BRI) mainly relies on the traditional and underdeveloped logistical trade routes including the terrestrial and oceanic...     

Investigating the impact of Glyoxal retrieval from MAX-DOAS observations during haze and non-haze conditions in Beijing

This study presents the Multi Axis Differential Optical Absorption Spectroscopy(MAXDOAS) measurements for Glyoxal(CHOCHO) in Beijing, China(39.95°N, 116.32°E). CHOCHO is the smallest compound of di-carbonyl group. As a primary sink of CHOCHO, its photolysis with NOx(oxides of nitrogen) results in the production of tropospheric ozone. Therefore,the focus of CHOCHO DOAS measurements is increasing in trend. We did the measurements from 09 May 2017 to 09 September 2017. The study was conducted to compare different retrieval settings in order to reveal best DOAS fit settings for CHOCHO;furthermore, effect of haze and non-haze days on CHOCHO concentration was examined.The root mean square of residual and Differential Slant Column density(dSCD) error was reduced when measurements were done with lower wavelength limit around 432–438 nm and upper intervals around 455–460 nm. Thus, lower wavelength intervals around432–438 nm and upper intervals around 457–460 nm were best for the retrieval of dSCDs for CHOCHO. Meteorological conditions like haze or non-haze days did not have significant effect on DOAS fit parameters. The CHOCHO vertical column densities range from 1.33 E +14 to 9.77 E + 14 molecules/cm2 during the study period with average of 6.16 E +14 molecules/cm2. The results indicated that during haze days CHOCHO concentration was higher because of lower rate of photolysis and atmospheric oxidation potential. Our results did not show any significant weekend effect on CHOCHO atmospheric concentration.     

Possible options to slow down the advancement rate of Tarbela delta

The pivot point of delta in Tarbela dam has reached at about 10.6 km from the dam face which may result in blocking of tunnels. Tarbela delta was modeled from 1979 to 2060 using hec-6 model. Initially, the model was calibrated for year 1999 and validated for years 2000, 2001, 2002, and 2006 by involving the data of sediment concentration, reservoir cross sections (73 range lines), elevation-area capacity curves, and inflows and outflows from the reservoir. Then, the model was used to generate future scenarios, i.e., run-1, run-2, and run-3 with pool levels; 428, 442, and 457 m, respectively, till 2060. Results of run-1 and run-2 showed advancement to choke the tunnels by 2010 and 2030, respectively. Finally, in run-3, the advancement was further delayed showing that tunnels 1 and 2 will be choked by year 2050 and pivot point will reach at 6.4 km from the dam face.     

Improving seabed classification from Multi-Beam Echo Sounder (MBES) backscatter data with visual data mining

Multi-Beam Echo Sounders are often used for classification of seabed type, as there exists a strong link between sonar backscatter and sediment characteristics of the seabed. Most of the methods for seabed classification from MBES backscatter create a highly-dimensional data set of statistical features and then use a combination of Principal Component Analysis and k-means clustering to derive classes. This procedure can be time consuming for contemporary large MBES data sets with millions of records. This paper examines the complexity of one of most commonly used classification approaches and suggests an alternative where feature data set is optimised in terms of dimensionality using computational and visual data mining. Both the original and the optimised method are tested on an MBES backscatter data set and validated against ground truth. The study found that the optimised method improves accuracy of classification and reduced complexity of processing. This is an encouraging result, which shows that bringing together methods from acoustic classification, visual data mining, spatial analysis and remote sensing can support the unprecedented increases in data volumes collected by contemporary acoustic sensors.     

Faecal and nitrate contamination in the groundwater of Mardan district,Pakistan

Environmental Geochemistry and Health - This study aimed to determine the status of groundwater contamination with faecal coliform and nitrate in the rural areas of Mardan district, Pakistan. Both...     

Rural energy transitions in developing countries: a case of the Uttam Urja initiative in India

In most developing countries, at the household level, traditional burning of biomass or use of inefficient technologies for domestic applications like lighting is common, triggering concerns related to fuel or technology switching. The paper focuses on opportunities to promote cleaner energy options through development of value chains delivering improved energy efficiency and access in developing countries. We discuss the example of Uttam Urja, a field project involving the dissemination of photovoltaic lighting technologies in rural areas of India. We focus on the challenges of introducing radical innovations into the residential energy sector in developing countries. For the purpose of this paper the Uttam Urja project is conceptualized as an ‘experiment’ and analysed using the Strategic Niche Management (SNM) framework. The paper emphasizes that to effect socio-technical transitions to clean energy options on the ground, it is desirable to focus on technology customization and innovative financing to cater to the needs and concerns of end users.     

Isolation of Cr(Ⅵ) reducing bacteria from industrial effluents and their potential use in bioremediation of chromium containing wastewater

The present study is aimed at assessing the ability of Bacillus sp.JDM-2-1 and Staphylococcus capitis to reduce hexavalent chromium into its trivalent form.Bacillus sp.JDM-2-1 could tolerate Cr(Ⅵ) (4800 μg/mL) and S.capitis could tolerate Cr(Ⅵ) (2800 μg/mL).Both organisms were able to resist Cd2+ (50 μg/mL),Cu2+ (200 μg/mL),Pb2+ (800 μg/mL),Hg2+ (50 μg/mL) and Ni2+ (4000 μg/mL).S.capitis resisted Zn2+ at 700 μg/mL while Bacillus sp.JDM-2-1 only showed resistance up to 50 μg/mL.Bacillus sp.JDM-2-1 and S.capitis showed optimum growth at pH 6 and 7,respectively,while both bacteria showed optimum growth at 37℃.Bacillus sp.JDM-2-1 and S.capitis could reduce 85% and 81% of hexavalent chromium from the medium after 96 h and were also capable of reducing hexavalent chromium 86% and 89%,respectively,from the industrial effluents after 144 h.Cell free extracts of Bacillus sp.JDM-2-1 and S.capitis showed reduction of 83% and 70% at concentration of 10 μg Cr(Ⅵ)/mL,respectively.The presence of an induced protein having molecular weight around 25 kDa in the presence of chromium points out a possible role of this protein in chromium reduction.The bacterial isolates can be exploited for bioremediation of hexavalent chromium containing wastes,since they seem to have the potential to reduce the toxic hexavalent form to its nontoxic trivalent form.