Variation in perception of environmental change in nine Solomon Islands communities: implications for securing fairness in community-based adaptation

Community-based approaches are pursued in recognition of the need for place-based responses to environmental change that integrate local understandings of risk and vulnerability. Yet the potential for fair adaptation is intimately linked to how variations in perceptions of environmental change and risk are treated. There is, however, little empirical evidence of the extent and nature of variations in risk perception in and between multiple community settings. Here, we rely on data from 231 semi-structured interviews conducted in nine communities in Western Province, Solomon Islands, to statistically model different perceptions of risk and change within and between communities. Overall, people were found to be less likely to perceive environmental changes in the marine environment than they were for terrestrial systems. The distance to the nearest market town (which may be a proxy for exposure to commercial logging and degree of involvement with the market economy), and gender had the greatest overall statistical effects on perceptions of risk. Yet, we also find that significant environmental change is underreported in communities, while variations in perception are not always easily related to commonly assumed fault lines of vulnerability. The findings suggest that there is an urgent need for methods that engage with the drivers of perceptions as part of community-based approaches. In particular, it is important to explicitly account for place, complexity and diversity of environmental risk perceptions, and we reinforce calls to engage seriously with underlying questions of power, culture, identity and practice that influence adaptive capacity and risk perception.

     

Gas chromatographic methods for monitoring of wastewater chlorophenol degradation in anaerobic reactors

Wastewater samples from an anaerobic reactor were extracted with hexane and derivatized with diazomethane (method 1) and with acetic anidride (method 2). Gas chromatography with electron-capture detection (ECD) was employed for separating the parent compound and intermediates trichlorophenols (TCP) and dichlorophenols (DCP) which originated from the penta chlorophenol (PCP) degradation process. The relations between concentrations of PCP, TCP and DCP areas were linear in the range of concentrations of 0.2 to 8 mg/L and 0.025 mg/L to 5 mg/L for methods 1 and 2, respectively. The repeatability of the extraction methods was satisfactory, with variation coefficients lower than 11%. For method 1, at the fortification level of 0.2 mg/L, recovery of PCP, TCP, and DCP was 112%, 74% and 45%, respectively. For method 2, the corresponding recovery values at the fortification level of 0.1 mg/L were 91%, 93% and 103%, respectively. Storage of the frozen samples did not alter their PCP determination properties. The chromatographic methods adapted for chlorophenol determination in wastewater were suitable with relatively simple manipulation techniques. The obtained results were reproducible and allowed identification of intermediates formed during the PCP degradation process.     

Extension of the EUROS Integrated Air Quality Model to Fine Particulate Matter by Coupling to CACM/MADRID 2

The European Operational Smog (EUROS) integrated air quality modelling system has been extended to model fine particulate matter (PM). From an extended literature study, the Caltech Atmospheric Chemistry Mechanism and the Model of Aerosol Dynamics, Reaction, Ionisation and Dissolution were selected and recently coupled to EUROS. Currently, modelling of mass and chemical composition of aerosols in two size fractions (PM_2.5 and PM_10–2.5) is possible. The chemical composition is expressed in terms of seven components: ammonium, nitrate, sulphate, elementary carbon, primary inorganic compounds, primary organic compounds and secondary organic compounds. Calculated PM₁₀ concentrations and chemical composition are presented for two summer months of the year 2003 (1 July to 31 August).     

Distribution of antifouling biocides in California marinas

Antifouling biocides are used to prevent the settlement and growth of organisms on submerged surfaces. Irgarol 1051 is currently among the most widely used organic booster biocides worldwide. This study reports Irgarol 1051, its major metabolite M1 (aka GS26575), and diuron concentrations found in selected California marinas. Seasonal water samples (n = 46) were collected during the summer and fall of 2006 from eleven marinas throughout Southern and Northern California. The samples were extracted using solid phase extraction and analysed utilizing liquid chromatography tandem mass spectrometry (LC-MS-MS) with electrospray ionization. All three compounds were detected in all samples, representing a 100% frequency of occurrence and indicating widespread use around the sampled marinas. Irgarol concentrations ranged from 12 to 712 ng L(-1) (average 102 ng L(-1)), M1 concentrations were 1-217 ng L(-1) (average 31 ng L(-1)), and diuron concentrations were 5-27 ng L(-1) (average 13 ng L(-1)). In general, concentrations of both Irgarol (15-712 ng L(-1)) and M1 (1-217 ng L(-1)) were greater in samples collected during the summer, corresponding to the peak of the boating season. The detected diuron concentrations in most cases were greater for fall samples (7-27 ng L(-1)), and probably represented a combination of non-agricultural (rights of way) and agricultural applications of diuron in California. The maximum Irgarol concentration detected in California marinas in summer 2006 (712 ng L(-1)) was five times greater than the Irgarol concentration suggested as the plant toxicity benchmark (136 ng L(-1)). Twenty three percent of samples from California marinas in this study exceeded this benchmark, suggesting that detected Irgarol concentrations may be high enough to cause changes in phytoplankton communities in the sampled marinas.     

A directional passive air sampler for monitoring polycyclic aromatic hydrocarbons (PAHs) in air mass

A passive air sampler was developed for collecting polycyclic aromatic hydrocarbons (PAHs) in air mass from various directions. The airflow velocity within the sampler was assessed for its responses to ambient wind speed and direction. The sampler was examined for trapped particles, evaluated quantitatively for influence of airflow velocity and temperature on PAH uptake, examined for PAH uptake kinetics, calibrated against active sampling, and finally tested in the field. The airflow volume passing the sampler was linearly proportional to ambient wind speed and sensitive to wind direction. The uptake rate for an individual PAH was a function of airflow velocity, temperature and the octanol-air partitioning coefficient of the PAH. For all PAHs with more than two rings, the passive sampler operated in a linear uptake phase for three weeks. Different PAH concentrations were obtained in air masses from different directions in the field test.     

Migration Pathways and Remediation of Urban Runoff for PAH Control

The migration pathways for polyaromatic hydrocarbons (PAH) and the associated active attenuating mechanisms within the urban environment are examined. The interest in developing an improved understanding of PAH migration/attenuation is focused toward quantification of the cost-effectiveness of urban runoff control strategies for mitigation of PAH inputs to receiving water bodies.A case study application to the City of Sault Ste Marie demonstrates that snow removal from sub-basins with the largest deposition rates, and relatively small wet and/or dry stormwater management ponds, are the most cost-effective remediation strategies.     

Analysis of pollutant levels in central Hong Kong applying neural network method with particle swarm optimization

Air pollution has emerged as an imminent issue in modernsociety. Prediction of pollutant levels is an importantresearch topic in atmospheric environment today. For fulfillingsuch prediction, the use of neural network (NN), and inparticular the multi-layer perceptrons, has presented to be acost-effective technique superior to traditional statisticalmethods. But their training, usually with back-propagation (BP)algorithm or other gradient algorithms, is often with certaindrawbacks, such as: 1) very slow convergence, and 2) easilygetting stuck in a local minimum. In this paper, a newlydeveloped method, particle swarm optimization (PSO) model, isadopted to train perceptrons, to predict pollutant levels, andas a result, a PSO-based neural network approach is presented. The approach is demonstrated to be feasible and effective bypredicting some real air-quality problems.     

Reduction of SO2 emissions by ammonia gas during unstaged combustion

The reduction of SO₂ by the addition of ammonia gas has been studied in a 2 m high fluidized bed combustor having a 30 cm static bed height and a freeboard height of 170 cm. Ammonia gas was injected at 52 cm above the distributor where the temperature is ca. 700° C by an uncooled stainless steel tube injector. Experiments were carried out to investigate the effects of amminia gas injection on sulphur dioxide emissions at unstaged conditions of: (i) excess air level, (ii) NH₃:SO₂ molar ratio, (iii) fluidizing velocity and (iv) bed height.A maximum reduction of 75% in SO₂ emissions was found at 40% excess air, at an NH₃:SO₂ molar ratio of 5.4. The onset of SO₂ reduction occurred at an NH₃:SO₂ ratio of 1.5 However, the most effective ratio was found to be between 3 and 5. Fluidizing velocity and bed height were also found to have significant influence on SO₂ reduction.It is difficult to determine how the SO₂ reduction varied with operating conditions. When ammonia is added in the main combustor zone, the temperature is much higher than that required for the occurrence of sulphur dioxide-ammonia and sulphur trioxide-ammonia reactions. However, this paper points out the significance of ammonia addition in the reduction of sulphur dioxide.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.