We investigated the issue of noise pollution in the Bidboland gas refineryby noise measurement and surveys. The Bidboland gas refinery has several process units.The sound pressure levels were measured at several places, such as units of 200, 300,400, 500, boiler, powerhouse area, cooling towers pumps, and maintenance area, and thecorresponding noise maps were produced by using sound plan software. We identified thesources of noise pollution. We first measured the mean A-weighted sound pressure levelin each study unit and analyzed the obtained data in Microsoft Excel. The noisiest unitswere identified and some suggestions were offered to reduce the sound exposure level. Itwas specifically noted through the surveys that the noise levels detected in all testedindustries was much above 80 dBA limit specified by regulations. In lieu, variousmeasures to control the noise pollution were contemplated and discussed.

An in situ particle imaging system for measurement of high concentrations of suspended particles ranging from 30 μm to several mm in diameter, is presented. The system obtains quasi-silhouettes of particles suspended within an open-path sample volume of up to 5 cm in length. Benchmarking against spherical standards and the LISST-100 show good agreement, providing confidence in measurements from the system when extending beyond the size, concentration and particle classification capabilities of the LISST-100. Particle-specific transmittance is used to classify particle type, independent of size and shape. This is applied to mixtures of oil droplets, gas bubbles and oil-coated gas bubbles, to provide independent measures of oil and gas size distributions, concentrations, and oil-gas ratios during simulated subsea releases. The system is also applied to in situ measurements of high concentrations of large mineral flocs surrounding a submarine mine tailings placement within a Norwegian Fjord. | publishedVersion

Air pollution is a leading public health concern around the world. Assessing the public's knowledge about air quality is critical to calibrate public health interventions. However, previous efforts to measure knowledge about air quality (AQIQ) have not relied on consistent and validated measures, thus precluding cross-country comparisons. We aimed to develop a robust scale to assess AQIQ and tested it in multiple countries. To evaluate the psychometric properties and select the best performing items out of 10 AQIQ questions, we used methods from classical test theory and item response theory. We evaluated the scales using several scalability measures, including the Kuder-Richardson Formula 20 (KR-20), Loevinger's H, as well as trace lines. Volunteers from the United States (US, n = 400), India (n = 403), and China (n = 443) were recruited to validate the scale. Multiple linear regression was used to estimate the association between demographic factors and AQIQ. We found that participants from India had the highest AQIQ. In addition, not all questions performed well in each country. The scale was pruned and shorter subscales were validated. In the US, we obtained a 4-item scale (KR20 = 0.53, Loevinger's H = 0.34). In India, we obtained a 6-item scale (KR20 = 0.56; Loevinger's H = 0.48 for just 2 items). In China, we obtained a 5-item scale (KR20 = 0.39; Loevinger's H = 0.41 for just 2 items). Compared to the 10-item scale, the pruned scales showed stronger associations between measures of socioeconomic status and AQIQ. The results were robust to the scale used. Overall, general knowledge questions measured AQIQ more effectively in the US and India whereas knowledge of the air quality index better measured AQIQ in China. The findings suggest that careful measurement and validation are essential to develop knowledge scales for use in public health and environmental research.