No abstract available.
Environmental Monitoring*

No abstract available.
Environmental Monitoring* ; Health Promotion*

Biomarkers ; Environmental Monitoring ; Humans

DECODA is a ecological model. Functions of DECODA include: research, analysis and management of population and community. Master file of DECODA contains species variables, community data and sample variables. DECODA includes 3 modules: MDS, DECORANA, TWINSPAN. Applying DECODA in analyzing medicinal plant (MP) gardens of Yao ethnicity in TWINSPAN classifies 56 MP gardens into 6 groups based on characteristics of MP: I, II, III, IV groups include MPs that fit with high sunlight, heavy soil, medium moisture and poor humus soil. V, VI groups are in opposition to the above groups. DECORANA classifies the MP gardens into 7 groups based on characteristics of gardens: I, II, III, IV groups have a favourable conditions for growing MP and the owner of the gardens pay more attention in growing MP. V, VI groups are in opposition to the above groups.
Environmental Monitoring ; Plants, Medicinal

Environmental Monitoring ; instrumentation

Environmental Monitoring ; Humans ; Occupational Health

China ; Environmental Monitoring ; legislation & jurisprudence ; Environmental Pollutants ; Organic Chemicals

Environmental Monitoring ; methods ; Environmental Pollutants ; analysis ; Microchip Analytical Procedures

This study was conducted to evaluate the relationships between the environmental exposure and biological monitoring among workers exposed to metallic mercury. We interviewed each workers to get the medical history including previous hazardous occupational history we measured the respiration rate and tidal volume of each worker in order to calculate the 8-hour inhaled mercury of workers. And we wanted to evaluate the effect of exposure duration to mercury concentrations in blood and urine as biologic exposure indices of metallic mercury. The regression and correlation analysis were done to the relationships of 8-hour inhaled mercury are mercury in blood and urine. The results were as follows; 1. The subjects were 35 fluorescent lamp manufacturing workers. The mean age of subjects was 24.8 years old, and the mean work careers of workers was 1.19 years. 89% of the total was consisted man. 2. The correlation coefficients between 8-hour inhaled mercury and mercury in blood and urine were higher than that of only considered air mercury concentration. 3. The correlation coefficients of 8-hour inhaled mercury and mercury in blood and urine were above 0.9 in workers who had exposed to mercury more than 1 year. 4. The R-square value and -value of regression analysis between the 8-hour inhaled mercury and mercury inn blood and urine was also higher in workers who had exposed to mercury over 1 year than in workers who had less than 1 year working experience. The important results or this study were that relationships between the 8hr-inhaled mercury and mercury in blood and urine was very high than that with air mercury concentration only. And the results were very apparent when considering workers 1 year or more. Therefore we concluded that the work career and respiratory volume of each individuals should be considered in evaluation the results of biological monitoring of workers exposed to metallic mercury.
Environmental Exposure ; Environmental Monitoring ; Inhalation* ; Respiratory Rate ; Tidal Volume

BACKGROUNDTask-based measurement (TBM) is a method to assess the eight-hour A-weighted equivalent noise exposure level (L(Aeq.8h)) besides dosimeter. TBM can be better used in factories by non-professional workers and staffs. However, it is still not clear if TBM is equal or similar with dosimeter for L(Aeq.8h) measurement in general. This study considered the measurement with dosimeter as real personal noise exposure level (PNEL) and assessed the accuracy of TBM by comparing the consistencies of TBM and dosimeter in L(Aeq.8h) measurement.

METHODSThe study was conducted in one automobile firm among 387 workers who are exposed to unstable noise. Dosimeters and TBM were used to compare the two strategies and assess the degree of agreement and causes of disagreement. Worker's PNEL was measured via TBM for noise; the real PNEL was also recorded. The TBM for noise was computed with task/position noise levels measured via sound level meter and workers' exposure information collected via working diary forms (WDF) filled by participants themselves. Full-shift noise exposure measurement via personal noise dosimeters were taken as the real PNEL. General linear model (GLM) was built to analyze the accuracy of TBM for noise and the source of difference between TBM for noise and real PNEL.

RESULTSThe L(Aeq.8h) with TBM were slightly higher than the real PNELs, except the electricians. Differences of the two values had statistical significance in stamping workers (P < 0.001), assembly workers (P = 0.015) and welding workers (P = 0.001). The correlation coefficient of L(Aeq.8h) with TBM and real PNELs was 0.841. Differences of the two results were mainly affected by real PNEL (F = 11.27, P = 0.001); and work groups (F = 3.11, P < 0.001) divided by jobs and workshops were also independent factors. PNEL of workers with fixed task/position ((86.53 ± 8.82) dB(A)) was higher than those without ((75.76 ± 9.92) dB(A)) (t = 8.84, P < 0.01). Whether workers had fixed task/position was another factor on the accuracy of TBM for noise (F = 4.36, P = 0.038).

CONCLUSIONTBM for noise has acceptable accuracy on workers' PNEL measurement. The accuracy is affected by job categories, workshops and variability of task/position. TBM for noise can yield a relatively conservative result of worker's PNEL in most cases, so it can be used to measure and assess workers' real PNEL.