Formaldehyde is added illegally to food to extend its shelf life due to its antiseptic and preservation properties. Several research has been conducted to examine the consequences of adulteration with formaldehyde in food items. These findings suggest that adding formaldehyde to food is considered harmful as it accumulates in the body with long-term consumption. In this review includes study findings on food adulteration with formaldehyde and their assessment of food safety based on the analytical method applied to various geographical regions, food matrix types, and their sources in food items. Additionally, this review sought to assess the risk of formaldehyde-tainted food and the understanding of its development in food and its impacts on food safety in light of the widespread formaldehyde adulteration. Finally, the study would be useful as a manual for implementing adequate and successful risk assessment to increase food safety.
The effect of morphology on the plasmonic sensing of the presence of formaldehyde in water by gold nanostructures has been investigated. The gold nanostructures with two different morphologies, namely spherical and rod, were prepared using a seed-mediated method. In typical results, it was found that the plasmonic properties of gold nanostructures were very sensitive to the presence of formaldehyde in their surrounding medium by showing the change in both the plasmonic peaks position and the intensity. Spherical nanoparticles (GNS), for example, indicated an increase in the sensitivity when the size was increased from 25 to 35 nm and dramatically decreased when the size was further increased. An m value, the ratio between plasmonic peak shift and refractive index change, as high as 36.5 nm/RIU (refractive index unit) was obtained so far. An expanded sensing mode to FD was obtained when gold nanostructures with nanorods morphology (GNR) were used because of the presence of two plasmonic modes for response probing. However, in the present study, effective plasmonic peak shift was not observed due to the intense plasmonic coupling of closely packed nanorod structures on the surface. Nevertheless, the present results at least provide a potential strategy for response enhancement via shape-effects. High performance plasmonic sensors could be obtained if controlled arrays of nanorods can be prepared on the surface.
Burning mosquito coils indoors generates smoke that can control mosquitoes effectively. This practice is currently used in numerous households in Asia, Africa, and South America. However, the smoke may contain pollutants of health concern. We conducted the present study to characterize the emissions from four common brands of mosquito coils from China and two common brands from Malaysia. We used mass balance equations to determine emission rates of fine particles (particulate matter < 2.5 microm in diameter; PM(2.5)), polycyclic aromatic hydrocarbons (PAHs), aldehydes, and ketones. Having applied these measured emission rates to predict indoor concentrations under realistic room conditions, we found that pollutant concentrations resulting from burning mosquito coils could substantially exceed health-based air quality standards or guidelines. Under the same combustion conditions, the tested Malaysian mosquito coils generated more measured pollutants than did the tested Chinese mosquito coils. We also identified a large suite of volatile organic compounds, including carcinogens and suspected carcinogens, in the coil smoke. In a set of experiments conducted in a room, we examined the size distribution of particulate matter contained in the coil smoke and found that the particles were ultrafine and fine. The findings from the present study suggest that exposure to the smoke of mosquito coils similar to the tested ones can pose significant acute and chronic health risks. For example, burning one mosquito coil would release the same amount of PM(2.5) mass as burning 75-137 cigarettes. The emission of formaldehyde from burning one coil can be as high as that released from burning 51 cigarettes.
Asthma is a chronic condition of great public health concern globally. The associated morbidity, mortality and healthcare utilisation place an enormous burden on healthcare infrastructure and services. This study demonstrates a multistage quantile regression approach to predicting excess demand for health care services in the form of asthma daily admissions in London, using retrospective data from the Hospital Episode Statistics, weather and air quality. Trivariate quantile regression models (QRM) of asthma daily admissions were fitted to a 14-day range of lags of environmental factors, accounting for seasonality in a hold-in sample of the data. Representative lags were pooled to form multivariate predictive models, selected through a systematic backward stepwise reduction approach. Models were cross-validated using a hold-out sample of the data, and their respective root mean square error measures, sensitivity, specificity and predictive values compared. Two of the predictive models were able to detect extreme number of daily asthma admissions at sensitivity levels of 76 % and 62 %, as well as specificities of 66 % and 76 %. Their positive predictive values were slightly higher for the hold-out sample (29 % and 28 %) than for the hold-in model development sample (16 % and 18 %). QRMs can be used in multistage to select suitable variables to forecast extreme asthma events. The associations between asthma and environmental factors, including temperature, ozone and carbon monoxide can be exploited in predicting future events using QRMs.