With the increase in population, people are moving more towards the prevalent technology, however various health implications are associated with such modern technologies. For example, modernization has enhanced noise levels and this causes some severe health issues. The land-use pattern in developing countries puts an adverse effect on health due to mixed land-use patterns and unorganized terrain parameters [
1]. This will not provide proper passage for the noise to propagate which leads to frequent noise impacts at a location. The annoyances in the city environment are affecting health significantly [
2]. The health implications like tinnitus, sleep disturbances, high BP are more prominent in people residing near the crossing having more exposure to road noise [
3]. The necessity to measure the levels of noise pollution in an area is increasing day by day and thus, various models are being derived by the researchers to showcase the relationship between the health implications and noise exposure with the help of confidence interval [
4]. Many pieces of evidence associate traffic noise with ischemic diseases or cardiovascular diseases, associations with blood pressure have been inconsistent about health effects of railway noise [
5,
6]. In special cases like the festive event, celebrations, marriages where the firecrackers are used leads to short span noise-sensitive events and it also has various health implications associated with it same as due to road noise [
7]. One of the main objectives that have to be derived is to propose a model to avoid the causes like sleep disturbances among people of old age [
8]. Major strategies that must be developed to tackle such issues necessitate accurate monitoring and analysis of noise sources and determining the propagation of noises to surrounding locations. The varied noise levels may be easily predicted using various empirical or semi-empirical models. The prediction of noise requires terrain data, noise data, and a prediction model. The terrain data dependency for noise modeling can be done through direct and indirect capturing with the camera, UAV systems, etc. [
9]. Monitoring of the noise is also difficult due to its transient nature and with better accuracy requires expensive Sound level meter-SPL. Smartphone-based noise capturing is a solution but the inaccuracy of data collection is a problem [
10]. However, the challenge of the collection of data for a large number of points can be resolved through prediction [
11]. An indirect method of retrieving the noise data from air pollution can be thought of for this SNSE. Efficient crowd-sourcing over the web offers a wide range of possibilities for exploring monitored data to map noise levels in different cities [
12]. The authors have tried to monitor and map the SNSE using the smartphone-based technique. The publishing of the map and showcasing the deterioration in noise level is also a need [
13]. The GIS-based mapping provides numerically simulated data through software programs that are primarily advanced for ambient noise mapping activities. It employs acoustic principles derived from one-of-a-kind resources [
14]. With the information provided above, the authors are conducting surveillance in the city of Lucknow. A cohesive investigation was conducted within the West-Bengal metropolis in which the time series and spatial noise distribution of traffic noise over the street and nearby buildings were acquired using the SPL and plotted on a web platform using the GIS [
15]. On the other hand, in Nigeria’s metropolitan areas, the same data became recorded and stimulated on the GIS platform. On the GIS platform, IDW interpolations were generated for the map, and one inspired variation became a set consistent with the WHO well-known for the annoyances with the use of spatial interpolation [
16]. Aside from that, a few people have employed questionnaire surveys to assess a location’s noise level. However, the polling results are limited to a specific sector and the inability to remember [
17]. They provided a completely average sense of noise for the area and its incapacity to remember the terrain parameters [
18], and different occasions over the period and location. The primary goal of the web-based GIS platform is to provide an accessible platform to generate the map and be easily viewable over the web platform with the user’s assistance. With the help of the GIS platform connecting with the web, various types of statistical or terrain parameters can be uploaded, with assistance to spatial reference, mapped, and can be easily accessed and tracked for data sharing [
19]. It includes a collection of web-based services that deliver information and capabilities to networked software programmed customers [
20]. GIS-based web platform serves as a platform for integration, encourages cross-organizational communication, and allows for better decision-making [
21]. In the case of web mapping basically, the data collected and mapped is to be stimulated over the web platform so that it can be seen by different users around the world. In the case of the web mapping technique, users around the organization of the world can collect the data and upload it on the same web platform [
22]. The flow chart shows the crowd-sourced collaborative mapping of noise data which can be published and can be seen by different users. The usage of Arc Info or ArcView software provided through a license enables the user to modify and convert the view to examine the data in the required format [
23]. The processing of the raster image through GIS while extracting the building roads etc. to generate the vector map [
24]. This web-based interactive server exposes users to a variety of websites by providing links to interactive GIS mapping and GIS data downloads [
10].
Researchers have utilized various models effectively to determine the noise data and to create noise maps. This modeling can be carried out using the following two techniques:
1.2. Indirect Method of Noise Modeling with Air Quality Data
Venkata et al. (2013) has investigated and performed experiments to get the variational changes in characteristics of aerosol, concentration, and radiative particles in the air due to firecrackers on Diwali [
27]. The assessment was based on six days of intensive analyses of various contaminants such as black carbon, particulate matter, and aerosol optical depth to capture the drastic variation in fireworks from pre-Diwali to post-Diwali. There has also been a major increase in gaseous air emissions such as SO
2 and NO
x levels that meet National Ambient Air Quality Standards [
28]. Such celebration-induced air pollution events can have major health repercussions, particularly for the respiratory and cardiovascular illnesses of the local population [
3]. Ambient air pollution is one of the most serious environmental health hazards. This is becoming more prevalent by the day and has a significant impact on human health. According to a 2017 survey, exposure to PM
2.5 was responsible for over 3 million fatalities worldwide. The use of fireworks at these activities produces smoke plumes, which can temporarily increase PM concentrations. The impact of using fireworks (and bonfires) has a negative effect on air quality, illumination, and human health. There is a lot of evidence showing that these events contain a lot of toxins, and as a result, there may be a lot of multipollutant radiation [
29]. Pollutant concentrations (such as PM
2.5, PM
10, and NO
x) increase more noticeably before and shortly after the fireworks display, followed by a return to baseline values, usually within 24 h. During fireworks show, peak pollutant concentrations can be 2–8 times higher than ambient levels. Thus the authors provide a list of potential research targets to better understand the effects of fireworks and bonfires on human and environmental health [
30].
In the case of noise mapping, we primarily are required to incorporate the terrain data and noise data. The noise data of different sources are integrated and then terrain and noise data are incorporated in the noise propagation model to predict the level of noise at the desired location. By developing a noise propagation model, we can make noise predictions for even those locations where we do not have any noise data. The predictions can be displayed as a noise map. The authors here have tried to do the same in this article. In this article, the authors have proposed a novel technique to predict the levels of noise even in short-term noise events like Diwali, wherein it is very difficult to collect relevant noise data. So, to solve this issue, the authors have tried to collect noise data indirectly using air quality. During Diwali night, noise levels and pollutants in the air increase in the same proportion. Therefore, by measuring the air pollutants level, the noise data can be collected and these data can be used to predict the levels of noise in a particular area.