The study aimed at evaluating or estimating the daily PM

The study aimed at evaluating or estimating the daily PM

The study aimed at evaluating or estimating the daily PM; PM10 and PM2.5 concentrations in California, before, during and after the 2003 wildfire. The researchers found that there was a 160 and 100 μg/m³ increase for PM; PM10 and PM2.5 respectively after the fires. They also found out that some environmental factors are strongly associated with the PM concentrations. The factors include relative humidity, some and Light extinction coefficient. The study used (IDW), kriging or cokriging for the interpolation. However, these methods could not be used during the fires because of heterogeneous pollution surfaces. In such cases, the researchers used satellite images for the smoke covered areas. As such, this study paved the way for other studies that could use satellite visibility and air quality measurements to determine particulate matter pollution.

The study faced two major challenges with interpolation. The first one was that the PM measurements were missing for the study period. The monitoring stations opened them on 3^rd and 6^th day, which meant that there was a large number of days that missed the PM data. The monitors were also frequently disabled when the stations had direct fire damage and therefore incapacitated. The second parts that made spatial interpolation impossible were that the PM concentrations produced by the fire were heterogeneous. The researchers suggest that some other methods or procedures could be used to fill the missing data, including spatial profiles, or co-located data.  However, the researchers could not use these methods because they were investigating erratic events (wildfire), which means that some stations could have higher smoke concentration than others. As such these two challenges made it very hard to collect and interpolate the data.

The researchers had various interpolating difficulties. The SUHI was hardly comparable with the Air UHI. This is because the Satellite-Derived LST is different from air temperature. The altitudes and observation principles are different. Secondly, cloud cover heavily affects the Satellite-Derived LST because of the internal sensor errors, r the view angle. The third problem was the method of calculation. In this case, there were several methods available for SUHI calculations. However, researchers found it very challenging to compare these measurements between cities because of the different SUHI estimates. Finally, the research identified that most studies have ignored the rapid changes in land cover. Thus, most previous studies had been using outdated data. Therefore, the researchers found it very hard to use this data to evaluate the SUHI trends in the past. As such, there were methodology challenges, data calculation or analysis challenges and environmental factors that hindered accuracy in the project.

The study was investigating the urban heat island in different urban patterns. In this case, it focused on Shenzhen Overseas Chinese Town (OCT) as the study area. It also proposes a new method that would integrate the GIS-based spatial interpolation and mobile measurements. The new method would express the local UHI intensities (LUHII) using temporal correction.  The results were SIMS and MAE of 0.3 °C.  The research also suggested that decreasing the building density would decrease the effects of LUHII. As such, the study provides theoretical support for various economic and environmental activities like eco-city construction, considering that the climate may differ over time.

The study aimed at evaluating or estimating the daily PM; PM10 and PM2.5 concentrations in California, before, during and after the 2003 wildfire. The researchers found that there was a 160 and 100 μg/m³ increase for PM; PM10 and PM2.5 respectively after the fires. They also found out that some environmental factors are strongly associated with the PM concentrations. The factors include relative humidity, some and Light extinction coefficient. The study used (IDW), kriging or cokriging for the interpolation. However, these methods could not be used during the fires because of heterogeneous pollution surfaces. In such cases, the researchers used satellite images for the smoke covered areas. As such, this study paved the way for other studies that could use satellite visibility and air quality measurements to determine particulate matter pollution.

The study faced two major challenges with interpolation. The first one was that the PM measurements were missing for the study period. The monitoring stations opened them on 3^rd and 6^th day, which meant that there was a large number of days that missed the PM data. The monitors were also frequently disabled when the stations had direct fire damage and therefore incapacitated. The second parts that made spatial interpolation impossible were that the PM concentrations produced by the fire were heterogeneous. The researchers suggest that some other methods or procedures could be used to fill the missing data, including spatial profiles, or co-located data.  However, the researchers could not use these methods because they were investigating erratic events (wildfire), which means that some stations could have higher smoke concentration than others. As such these two challenges made it very hard to collect and interpolate the data.

The researchers had various interpolating difficulties. The SUHI was hardly comparable with the Air UHI. This is because the Satellite-Derived LST is different from air temperature. The altitudes and observation principles are different. Secondly, cloud cover heavily affects the Satellite-Derived LST because of the internal sensor errors, r the view angle. The third problem was the method of calculation. In this case, there were several methods available for SUHI calculations. However, researchers found it very challenging to compare these measurements between cities because of the different SUHI estimates. Finally, the research identified that most studies have ignored the rapid changes in land cover. Thus, most previous studies had been using outdated data. Therefore, the researchers found it very hard to use this data to evaluate the SUHI trends in the past. As such, there were methodology challenges, data calculation or analysis challenges and environmental factors that hindered accuracy in the project.

The study was investigating the urban heat island in different urban patterns. In this case, it focused on Shenzhen Overseas Chinese Town (OCT) as the study area. It also proposes a new method that would integrate the GIS-based spatial interpolation and mobile measurements. The new method would express the local UHI intensities (LUHII) using temporal correction.  The results were SIMS and MAE of 0.3 °C.  The research also suggested that decreasing the building density would decrease the effects of LUHII. As such, the study provides theoretical support for various economic and environmental activities like eco-city construction, considering that the climate may differ over time.