BackgroundOccupational risk factors are one of the major causes of respiratory illnesses and symptoms, and account for 13% of chronic obstructive pulmonary disease and 11% of asthma worldwide. Majority of brick kilns in Pakistan use wood and coal for baking the bricks which makes the brick kiln workers susceptible to high exposure of air pollution. This study was designed to describe frequency of chronic respiratory symptoms and illnesses and study the association between these symptoms and different types of work.MethodsThis was a questionnaire based cross sectional survey conducted among the brick kiln workers in Larkana and Dadu districts, Sindh, Pakistan. A total of 340 adult men were assessed using translated version of the American Thoracic Society Division of Lung Disease (ATS-DLD) questionnaire. Logistic regression analysis was done to determine the relationship between various socio-demographic and occupational factors (age, education, type of work, number of years at work, smoking status), and the respiratory symptoms and illnesses (chronic cough, chronic phlegm, wheeze, Chronic Bronchitis and asthma).ResultsResults of the study show that 22.4% workers had chronic cough while 21.2% reported chronic phlegm. 13.8% had two or more attacks of shortness of breath with wheezing. 17.1% workers were suffering from Chronic Bronchitis while 8.2% reported physician diagnosed asthma. Amongst the non-smoking workers 8.9% had Chronic Bronchitis. Multivariate analysis found that workers involved in brick baking were more likely to have Chronic Bronchitis (OR= 3.7, 95% CI 1.1-11.6, p=<0.05) and asthma (OR= 3.9, 95% CI 1.01-15.5, p=<0.05) compared to those involved in carriage and placement work.ConclusionA high frequency of respiratory symptoms and illnesses was observed among brick kiln workers. Age, nature of work and smoking were strong predictors of developing these symptoms and illnesses.

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The integration of distribution generation (DG) in distribution networks with improper planning adversely influences the quality of the electrical networks. Conventionally, the outputs from the intermittent DGs, such as solar photovoltaic (PV) plants, are assumed dispatchable. The intermittency of solar irradiance on the outputs of the PV modules has been ignored in most studies on the sizing and placement of DGs. By looking at this problem, this paper presents the sizing and placement of a distributed solar photovoltaic plant (DSPP) by using time series historical weather data. To predict the output from the PV modules, 15 years of solar data were modeled with the aid of a beta probability density function. The total energy loss index was formulated as the main objective function, and the optimization problem was solved by mixed integer optimization by using genetic algorithm. By adopting a time-varying commercial load, the proposed algorithm was applied on IEEE 33 bus and IEEE 69 bus distribution networks. The numerical studies on the two distribution networks show the advantages of the proposed approach for minimizing the total energy losses and improving the bus voltage profiles. It was revealed that up to 38% of the total energy losses in distribution networks could be reduced at sites with solar insolation of 5.65 peaks sun hours. In contrast to existing methods, planning for DGs by using weather data provided more realistic results for DSPP in distribution networks.