Conventional air quality monitoring systems, such as gas analysers, are commonly used in many developed and developing countries to monitor air quality. However, these techniques have high costs associated with both installation and maintenance. One possible solution to complement these techniques is the application of low-cost air quality sensors (LAQSs), which have the potential to give higher spatial and temporal data of gas pollutants with high precision and accuracy. In this paper, we present DiracSense, a custom-made LAQS that monitors the gas pollutants ozone (O₃), nitrogen dioxide (NO₂), and carbon monoxide (CO). The aim of this study is to investigate its performance based on laboratory calibration and field experiments. Several model calibrations were developed to improve the accuracy and performance of the LAQS. Laboratory calibrations were carried out to determine the zero offset and sensitivities of each sensor. The results showed that the sensor performed with a highly linear correlation with the reference instrument with a response-time range from 0.5 to 1.7 min. The performance of several calibration models including a calibrated simple equation and supervised learning algorithms (adaptive neuro-fuzzy inference system or ANFIS and the multilayer feed-forward perceptron or MLP) were compared. The field calibration focused on O₃ measurements due to the lack of a reference instrument for CO and NO₂. Combinations of inputs were evaluated during the development of the supervised learning algorithm. The validation results demonstrated that the ANFIS model with four inputs (WE OX, AE OX, T, and NO₂) had the lowest error in terms of statistical performance and the highest correlation coefficients with respect to the reference instrument (0.8 < r < 0.95). These results suggest that the ANFIS model is promising as a calibration tool since it has the capability to improve the accuracy and performance of the low-cost electrochemical sensor.

Keywords: Land surface temperature, Land cover, Remote Sensing, Geographical Information System Land cover type is an important signature that is usually used to understand the interaction between the ground surfaces with the local temperature. Various land cover types such as high density built up areas, vegetation, bare land and water bodies are areas where heat signature are measured using remote sensing image. The aim of this study is to analyse the impact of land surface temperature on land cover types. The objectives are 1) to analyse the mean temperature for each land cover types and 2) to analyse the relationship of temperature variation within land cover types: built up area, green area, forest, water bodies and bare land. The method used in this research was supervised classification for land cover map and mono window algorithm for land surface temperature (LST) extraction. The statistical analysis of post hoc Tukey test was used on an image captured on five available images. A pixel-based change detection was applied to the temperature and land cover images. The result of post hoc Tukey test for the images showed that these land cover types: built up-green, built up-forest, built up-water bodies have caused significant difference in the temperature variation. However, built up-bare land did not show significant impact at p<0.05. These findings show that green areas appears to have a lower temperature difference, which is between 2° to 3° Celsius compared to urban areas. The findings also show that the average temperature and the built up percentage has a moderate correlation with R² = 0.53. The environmental implications of these interactions can provide some insights for future land use planning in the region.

The majority of atmospheric measurements of volatile organic compounds (VOCs) are usually limited to a small range, either in volatility or time resolution. A combined heart-cut gas chromatography (GC) with comprehensive two-dimensional GC (GC×GC) instrument was developed, specifically to increase the number of VOCs analysed using a single instrument. The system uses valve based modulation and was fully automated, making it suitable for use in the field. A laboratory comparison to an existing dual-channel GC (DC-GC) instrument demonstrated that this new GC-GC×GC can accurately measure atmospheric mixing ratios of C ${}_5$ -C ${}_{13}$ VOC species with a wide range of functionalities. Approximately hourly field measurements were conducted at a remote marine atmospheric research station in Bachok, Malaysia. This region was shown to be influenced by clean marine air masses, local anthropogenic and biogenic emission sources and aged emissions transported from highly polluted South East Asian regions. A dramatic shift in air mass direction was observed each day associated with the development of a sea breeze, which influenced the diurnal profiles of species measured at the Bachok site. A proton-transfer-reaction mass spectrometer (PTR-MS) was also deployed at Bachok and compared to the new GC-GC×GC instrument. Overall, the GC-GC×GC instrument has been shown to perform well in lab comparisons and during field observations. This represents a good compromise between volatility and high complexity $online$ measurements of VOCs.

Urbanization leads to positive and negative impacts on the society. While it accelerates economic growth, it also brings negative impact to the environment and people‘s health. The growth of urbanization is unavoidable; therefore, sustainability needs to be maintained to preserve the liveability of urban dwellers. The intention of this study is to review the use of climatic information in planning future developments in the local land use map. The studies of content and interview method were done in order to understand the real situation of this environmental mitigation. In further, the identification of the gaps of e study. The findings of this study were used to propose the climate indicators for the existing land use map. Future studies may focus on conducting preliminary experiments to apply the urban climatic map in the local context.

The gut microbiomes of Antarctic penguins are important for the fitness of the host birds and their chicks. The compositions of microbial communities in Antarctic penguin guts are strongly associated with the birds’ diet, physiological adaptation and phylogeny. Whilst seasonal changes in food resources, distribution and population parameters of Antarctic penguins have been well addressed, little research is available on the stability or variability of penguin stomach microbiomes over time. Here, we focused on two Pygoscelis penguin species breeding sympatrically in the maritime Antarctic and analysed their stomach contents to assess whether penguin gut microbiota differed over three austral summer breeding seasons. We used a high-throughput DNA sequencing approach to study bacterial diversity in stomach regurgitates of Adélie (Pygoscelis adeliae) and chinstrap (Pygoscelis antarctica) penguins that have a similar foraging regime on Signy Island (South Orkney Islands). Our data revealed significant differences in bacterial alpha and beta diversity between the study seasons. We also identified bacterial genera that were significantly associated with specific breeding seasons, diet compositions, chick-rearing stages and sampling events. This study provides a baseline for establishing future monitoring of penguin gut microbiomes in a rapidly changing environment.