Finally, based on our implementation, a solution is identified to detect the affected degree of the environment and the margin of the healthy environment. Muzahidul Islam, Professor, Department of Computer Science and Engineering, United International University, for his great guidance, support, motivation and review of my project.
Definition, Opportunity, and Capability of Data An- alytics
What is Shipbreaking Industry?
History of Shipbreaking Industry in Bangladesh
Present State of the Problem
Standard Air Quality Index (AQI)
To better understand what local air quality means to our health, AQI was created. Moderate: AQI is somewhere in the range of 51-100, which means the air quality is ok.
Motivation
Study Area
Research Challenge
Objective of the study
Scope of the study
Contribution
Organization of this report
Background
Literature Review
The contribution of the framework was created for productive monitoring of air quality with continuous information. The research gap uses RF-based ML algorithms to make air quality adjustments at minimal cost. Indoor air quality monitoring systems for improved living environments: a review towards sustainable smart cities.
Review the state-of-the-art for indoor air quality monitoring using IoT and wireless networks from 2014–2019. Integrated wireless sensors, gateways and IoT server were part of the disclosed modular end-to-end indoor air quality monitoring method. Real-time localized air quality monitoring and forecasting through mobile and fixed IoT sensing network.
Deep learning model with an accurate and precise air quality index value at the designated city location. A machine learning calibration model using random forests to improve sensor performance for low-cost air quality monitoring. In the previous work, some articles worked on indoor air quality monitoring in various industries or hospitals, and some articles investigated the impact of the shipbreaking industry on the environment or workers' health conditions.
Sensor Layer
Reporting over the I2C protocol usually takes place between two devices, one acting as the expert and the other as the slave. The DHT22 is a minimal-cost mechanized temperature and viscosity sensor that works as a digital sensor. It gives the most essential flexibility to the manufacturer and can be changed in accordance with the needs regarding assessment time, accuracy and power consumption by browsing endless expected mixes of the device settings.
Gas sensor: A gas sensor detects the presence or collection of gases in the environment and works as an analog sensor. The sensor gives a glimpse of potential conditions by changing the resistance of the substance inside the sensor, which can be estimated as the subsequent voltage, taking into account the mixing of the gas. The type and collection of the gas can be judged in light of this voltage issue.
The chemical resistor's ability to conduct current determines a gas sensor's maximum ability to detect gases. For the calculation of the PM2.5 in the air, these three sensors were used in our system. Using the above hardware devices, we prepared our circuit diagram, which is shown in figure 3.3.
Network Layer
Data Processing Layer
The head of the CMCH suggested to go to Sitakunda Upazila Health Complex and referred us to the HMO of that hospital. According to them, most of the patients in the Sitakunda area are affected by fever, breathing problems and lung cancer, and most of the patients are adult men. By going there and talking to the doctor, we found that fever, breathing problems and cancer patient percentage is 30-40% and most of the patients are children and the rate is high in winter season, medium in summer season, and low in the rainy season.
In the same procedure, we performed a doctor's examination and collected the patient history in that area. Since it is one of the best private medical hospitals in Chattogram division and the number of admitted patients is too high, we choose it. We collected the register book from this hospital with the permission of the head of the Medicine department and the head of the Oncology department.
Two parts of a tree that can be used for interpretation are nodes and leaves. The way decision trees are built in a forest is different from a random forest classifier. The original training trial is used to build each decision tree in the forest of additional trees.
Application Layer
In this system, we set up IoT devices and read the real-time value of temperature, humidity, surface pressure, dew point and PM2.5 from IoT sensors. Also, previous data was collected from different sites and conducted a hospital survey to determine the health condition of workers in the Sitakunda ship breaking industry. Data analysis technique was applied to pre-process data, classify the model and find the best model.
The use of various ML algorithms predicts the best accuracy and analysis of the risk factor is high, medium or low. We discuss how the system is basically implemented and the experimental result of our system in this chapter. The sensor that transmits data, gets data, implements algorithms with test accuracy, and the current position of the system is examined and shown below.
One of the most devastating natural disasters to hit the planet is climate change. This region has had some adverse natural disasters over the preceding 10 years due to the climate issue.
IoT Result
We choose the southeast coastal region of Bangladesh as it is a prominent location for the ship breaking industry. The next parameter is AQI and for these we used three different air quality sensors called MQ2, MQ7 and MQ135. Since the D1 mini has only one analog sensor, we have presented one sensor directly on the D1 mini and the other two are presented with the help of the ADS1115.
This device is mainly based on the I2C protocol, where the BMP sensor is used as the 0X76 register, and the other as the default register of each sensor. DHT22 was used for temperature, humidity and dew point parameters and for AQI we used three types of gas sensors to identify the AQI of the current area which is Sitakunda. After setting up all the sensors, we connected our model to the laptop/computer with a cable and connected Wi-Fi.
After connecting, we ran the code, compiled it, and uploaded it to the cloud server called ThingSpeak. Using IoT sensors, we collected real-time data from specific locations and took our sensors to various conditions to see how much data we get from our device. Here "field1" represents the "temperature", "field2" represents the "Humidity", "field3" represents the "dew point", "field4" represents the "surface pressure" and "field5" represents the "PM2.5" .
Past Data Analysis
For our final experiment, we considered the hospital data of the Sitakunda Health Complex as our research field in the Sitakunda area. According to this health assessment, we found that people in the shipbreaking industry are severely affected in the winter season because air pollution lasts much longer than in any other season. We have collected sample datasets for environmental pollution analysis and worker health observation, where the risk factor is the dependent variable and temperature, humidity, dew point, surface pressure and PM2.5 are the independent variables.
We detailed our dataset after taking input data to find outliers and significant features. Because of this, we have calculated the values of our independent and dependent variables' means, standard deviations, minimum values, Q1, Q2, Q3 and maximum, which are shown in Figure 4.6. Our collected dataset is a categorical value, so we have used the convert method to convert it to an integer value.
Here we have drawn a plot bar to show our target value (risk factor), where the value. Then we have counted the number of noise or missing values in our dataset to preprocess it. Here our original data has been 2373; but after filtering the entire dataset we have found 2302 data with 5 independent variables and 1 dependent variable.
Interface
Discussion
Security and health issues such as hoods, safety shoes, glasses, gloves or access to the medical care of the laborers can be ensured during the working period. The managers of shipbreaking tasks and governmental and non-governmental organizations can use the review findings to promote a positive work space to guarantee workers' health. If these strategies will all be carried out, it will expand the seriousness of the shipbreaking industry in Bangladesh, further develop workers' health conditions, reduce climate pollution damage and contribute to building a green state.
Today this business has become one of the most productive and fast growing organizations in Bangladesh and everywhere. We have carried out an assessment of the material flow of steel in Bangladesh with a focus on ship breaking activities and have already achieved one of the best conditions in the national economy [49]. Due to the expansion of the ship breaking industry in recent years, Bangladesh's coastal environment has been damaged [51].
Our primary goal is to build a framework that can continuously monitor the quality of the environment with the observation of the health status of the workers and determine that the environment in the shipbreaking industry is not safe for them. Our study area is the south-eastern part of Bangladesh, which is one of the most important businesses for our country. In addition, we have conducted a health survey (risk factor) using a time series system from various hospitals in the Chattogram region to assess the actual state of health of the ship.
Future Work
Shouman, “Iot-enabled system for enhanced air quality monitoring and edge prediction,” Complex. Marques, “Internet of Things-based indoor air quality monitoring systems: a systematic review,” International Journal of Environmental Research and Public Health , vol. Hong, “Indoor air quality monitoring systems for improved living environments: a review towards sustainable smart cities,” Sustainability, vol.
Han, “Development of an IoT-based indoor air quality monitoring platform,” Journal of Sensors, vol. Marques, “Sensors for indoor air quality monitoring and assessment through the Internet of Things: a systematic review,” Environmental Monitoring and Assessment, vol. Woo, “Real-time localized air quality monitoring and prediction through mobile and fixed iot sensor network,” IEEE Access , vol.
Tsai, “Construction and Application of an Intelligent Air Quality Monitoring System for a Healthcare Environment,” Journal of Medical Systems, Vol. Luhach, "IoT Based Air Quality Montage System Using MQ135 and MQ7 with Machine Learning Analysis," Scaleble Computing: Practice and Experience, vol. Khadar, "IoT Based Real-time Air Quality Monitoring and Control System for Improving Health and Safety of Industrial Workers", International Journal of Innovative Technology and Exploring Engineering, Vol.
