This study evaluated the extractability and bioavailability of Phosphorus (P) recovered from waste activated sludge (WAS) so as to reduce dependence on the import of non-renewable P resources. P extraction was carried out using sulfuric acid (H2SO4). A response surface methodology was used to optimize conditions for the chemical leaching of WAS. The results showed the optimum condition for leaching WAS with 0.1 mol/L H2SO4 for 30 min, resulting in 97% P released. The efficiency of P recovery by P precipitation was associated with pH value and Mg:P. At pH 7, 9, and 11, P recovery was 92, 92, and 91% with uncontrolled Mg and 93, 93, and 92% with sea salt (Mg:P, 2:1), respectively. However, the yield of the produced struvite was much lower compared with that of added sea salt. From elemental analysis, the yield of struvite precipitated at pH 9 of Mg:P, 2:1 was about 26%, and the total P content of the precipitate was 12%. Available P was almost 80% after 35 days of operation, which was higher than that of commercial fertilizers. Results of this study are expected to provide fully comprehensive information to decision-makers regarding the suitability of implementing P-composite matter recovered from WAS. This will also help close the loop of the P cycle for food cultivation in the human ecosystem.
Road traffic is the most significant source of noise in an urban city and is considered not only an environmental nuisance but also a threat to public health. Therefore, this study aimed to determine road traffic noise levels in Phuket Province, including Muang Phuket, Thalang, and Kathu District; and to compare them with predicted noise levels using NMTHAI 1.2. Traffic noise level, traffic volume and speed of vehicles were measured on main roads including Yaowarat, Ratsada, Montri, Patipat, Ban Muangmai, Ban Kain, Ban Lipon, Baramee and Vichitsongkram Road. The results showed that traffic noise in Muang Phuket, Thalang and Kathu Districts were 70.0-70.9, 72.9-74.7 and 74.6-74.8 dBA, respectively. The result revealed that traffic noise levels obtained from the model were higher than measured noise at an average of 4.82.3 dBA. A high correlation was observed between predicted and measured traffic noise levels (R 2 = 0.655, P < 0.01). Speed of vehicles and traffic volume were key variables affecting traffic noise level with a correlation coefficient of 0.752 and 0.702 at 99% confidence level, respectively. The model performed reasonably well under different traffic noise conditions and could predict traffic noise of other cities in Thailand.
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