Tourism: economic development without increasing CO2 emissions in Pakistan

Environment, Development and Sustainability - Globally, the national and cultural resources of a country are major sources of attraction for tourism. Pakistan is gifted with abundant natural and...     

Aerosol chemical characteristics of an island site in the Bay of Bengal (Bhola-Bangladesh)

Aerosol constituents (elemental carbon, organic carbon, soluble ions including organic acids, and selected trace metals) were investigated from samples of a field campaign taking place at Bhola Island in the Bay of Bengal (Bangladesh). The campaign took place in the pre-monsoon season (May 2001) using low volume samplers. Carbonaceous material comprised the majority of the analysed components. The average concentrations of EC and OC were 2.8 and 4.6 microg m(-3), respectively. Oxalic acid was the most abundant dicarboxylic acid (average 268 ng m(-3)) followed by malonic and malic acid. The contribution of carboxylic acids-carbon to organic carbon was 2.0%. Average concentrations observed for sulfate and nitrate were 3.7 microg m(-3) and 1.5 microg m(-3). Two different types of aerosol were identified at the rural background site on Bhola Island during southerly synoptic flow by means of trajectory analysis: air masses were transported from the Bay of Bengal to the sampling site in all cases. However, during "Period 1" they experienced longer residence times over the Indian Ocean, while the "Period 2" trajectories came along the Indian coast or passed over the Indian continent. During Period 1 the concentration levels of soluble ions were a factor of 4-6 lower than during Period 2. The concentrations of EC, OC and K differed less than a factor of 1.5 between the two periods. The Period 1 aerosol showed similarities to the haze layers observed during winter-monsoon conditions south of India during the INDOEX experiment. Based on EC/TC and K/EC ratios we find that around 80% of the carbonaceous aerosol from Period 1 in Bhola is from fossil fuel and only around 50% from Period 2. Absolute concentrations of carbonaceous species, soluble ions and trace metals indicate that the background site on Bhola Island is affected by emissions from urbanized regions of Southeast Asia.     

Influence of canola and sunflower diet amendments on cattle feedlot manure

Cattle (Bos taurus) producers can replace a part of the traditional diet of barley (Hordeum vulgare L.) grain/silage with sunflower (Helianthus annus L.) seeds or canola meal (Brassica napus L.)/oil to enhance conjugated linoleic acids (CLA) content in milk and meat for its positive health benefits. The objective of this study is to investigate the effects of feeding sunflower or canola to finishing steers on cattle manure chemical properties and volatile fatty acid (VFA) content. The control diet contained 84% rolled barley and 15% barley silage, which provided only 2.6% lipid. The other six treatments had 6.6 to 8.6% lipid delivered from sources such as hay, sunflower seed (SS), canola meal/oil, and SS forage pellets. Manure samples (a mixture of cattle urine, feces, and woodchip bedding materials) were collected and analyzed after cattle had been on these diets for 113 d. The dietary source and level of lipid had no effect on organic N and nitrate N content in manure, but significantly affected ammonia N and VFA. Inclusion of SS forage pellets, hay, or canola meal/oil in cattle diets had no significant impact on manure characteristics, but SS significantly reduced the pH and increased propionic, isobutyric, and isovaleric content. In addition, N loss after excretion (mainly from urine N) increases with the pH and N levels in both feed and manure. The combination of SS with barley silage resulted in a lower VFA and NH3 content in manure and should be a more attractive option. To better manage N nutrient cycles and reduce NH3 related odor problems, feed and manure pH should be one of the factors to consider when determining feed mix rations.     

Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions

Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.     

Nanocatalyzed biodiesel synthesis from the oily contents of Marine brown alga Dictyota dichotoma

This research article demonstrates biodiesel synthesis through the methanolysis of the oily contents (4.02 ± 0.27% w/w on dried basis) of Dictyota dichotoma collected from the coast of Hawksbay, Pakistan. The metal oxides (CaO, MgO, ZnO, and TiO₂) used as nanocatalysts were refluxed (5% K₂SO₄), calcinated (850 °C) and characterized by Atomic Force Microscopy (AFM) which produced 93.2% w/w FAME (biodiesel) at relatively mild condition (5% catalyst, 65 °C, 3 h, 18:1 molar ratio) using CaO. Whereas, MgO, ZnO, and TiO₂ produced 92.4%, 72.5%, and 31.8% w/w FAME, respectively at elevated condition (225 °C). Thus, CaO was considered to be the best catalyst among the others. This tri-phase reaction require continuous fast mixing and the yield depends on the reaction parameters like catalyst amount, temperature, reaction time and molar ratio (methanol: oil). The reusability of these heterogeneous catalysts simplified the purification step, reduced the waste generation and make the final product technically and economically viable.     

A context-intensive approach to imputation of missing values in data sets from networks of environmental monitors

Although networks of environmental monitors are constantly improving through advances in technology and management, instances of missing data still occur. Many methods of imputing values for missing data are available, but they are often difficult to use or produce unsatisfactory results. I-Bot (short for “Imputation Robot”) is a context-intensive approach to the imputation of missing data in data sets from networks of environmental monitors. I-Bot is easy to use and routinely produces imputed values that are highly reliable. I-Bot is described and demonstrated using more than 10 years of California data for daily maximum 8-hr ozone, 24-hr PM_2.5 (particulate matter with an aerodynamic diameter <2.5 μm), mid-day average surface temperature, and mid-day average wind speed. I-Bot performance is evaluated by imputing values for observed data as if they were missing, and then comparing the imputed values with the observed values. In many cases, I-Bot is able to impute values for long periods with missing data, such as a week, a month, a year, or even longer. Qualitative visual methods and standard quantitative metrics demonstrate the effectiveness of the I-Bot methodology.Implications: Many resources are expended every year to analyze and interpret data sets from networks of environmental monitors. A large fraction of those resources is used to cope with difficulties due to the presence of missing data. The I-Bot method of imputing values for such missing data may help convert incomplete data sets into virtually complete data sets that facilitate the analysis and reliable interpretation of vital environmental data.     

The effect of nutrients shortage on plant’s efficiency to capture solar radiations under semi-arid environments

Radiation use efficiency (RUE) is considered critical for calculation of crop yield. The crop productivity can be improved by increasing the interception of solar radiation and maintaining higher RUE for plants. Irrigation water and nitrogen (N) supply are the main limiting factors for RUE in maize (Zea mays L.) across the semi-arid environments. Field experiments were conducted during two consecutive growing seasons (2009–2010) to optimize RUE in relation to N application timings and rates with varying irrigation water management practices. In experiment 1, three N application timings were made, while in experiment 2, three possible water management practices were used. In both experiments, five N rates (100, 150, 200, 250, and 300 kg N ha⁻¹) were applied to evaluate the effects of irrigation water and N on cumulative photosynthetic active radiation (PARi), dry matter RUE (RUE_DM), and grain yield RUE (RUE_GY). The results demonstrated that cumulative PARi and RUEs were not constant during the plant growth under varying the nutrients. The water and N significantly influenced cumulative PARi and RUEs during the both growing seasons. In experiment 1, the maximum cumulative PARi was observed by application of 250 kg N ha⁻¹ in three splits (1/3 N at V2, 1/3 N at V16, and 1/3 N at R1 stage), and the highest RUE_DM was achieved by the application of 300 kg N ha⁻¹. However, the highest RUE_GY was observed by application of 250 kg N ha⁻¹. In experiment 2, the maximum cumulative PARi was attained at normal irrigation regime with 250 kg N ha⁻¹, while the highest RUE_DM and RUE_GY were recorded at normal irrigation regime with the application of 300 kg N ha⁻¹. The regression analysis showed significant and positive correlation of RUE_GY with grain yield. Therefore, optimum water and N doses are important for attaining higher RUE, which may enhance maize grain yield semi-arid environment; this may be considered in formulating good agricultural practices for the environmental conditions resembling to those of this study.