Emissions detection system and methods

1. An operating emissions quantification method for quantifying emissions of a target substance from operating devices at a site, the method comprising:

providing a first air quality monitor comprising:

a first sensor responsive to the target substance; and

a first location at which the first air quality monitor is located on the site;

measuring a first set of onsite parameters with the first air quality monitor over a period of time to obtain a plurality of individual measurements of each parameter of the first set of onsite parameters, wherein the plurality of individual measurements of the first set of onsite parameters comprises:

a first measured substance concentration of the target substance measured with the first air quality monitor; and

a first set of individual atmospheric readings;

transmitting the first measured substance concentration to a first server;

transmitting the first set of individual atmospheric readings to the first server;

procuring a height of planetary boundary layer (hPBL) data for the site from a second server;

training a prediction model associated with the first air quality monitor:

obtaining a plurality of first predicted substance concentrations of the target substance corresponding to the first air quality monitor from the prediction model, with at least:

the first set of individual atmospheric readings; and

the hPBL data for the site;

wherein the plurality of first predicted substance concentrations is obtained over a predefined period at a predefined frequency;

generating a mapping of a weighted mean of the plurality of first predicted substance concentrations grouped in each feature group of a predetermined number of feature groups, wherein the predetermined number of feature groups together are representative of feature values over a predetermined range;

obtaining a location map of a plurality of emission sources at the site, the location map comprising a location and an identity associated with each of the plurality of emission sources;

for each emission source of the plurality of emission sources:

generating a simulated plume model, based on a wind-direction; and

calculating a plurality of representative circular normal distributions for each air quality monitor, using the simulated plume model, by setting a plurality of presumed flux values to the simulated plume model;

performing an analysis of the plurality of representative circular normal distributions in relation with the mapping to identify a relevant representative circular normal distribution from the plurality of representative circular normal distributions, wherein the relevant representative circular normal distribution is indicative of a target emission source; and

determining the target emission source, based on the analysis and the location map.

2. The operating emissions quantification method of claim 1 , wherein the hPBL data further comprises:

a trapping condition corresponding to wind stagnation;

wherein during emission of the substance from an emission source, the trapping condition is associated with a gradual accumulation of the substance in an atmosphere surrounding the site and a higher first measured substance concentration of the target substance measured with the first air quality monitor.

3. The operating emissions quantification method of claim 1 , wherein the second server is High Resolution Rapid Refresh (HRRR) maintained by National Oceanic and Atmospheric Administration (NOAA).

4. The operating emissions quantification method of claim 3 further comprising procuring the hPBL data from the second server periodically at a predetermined frequency,

wherein the hPBL data is refreshed on an hourly-basis to a resolution of 3 kilometers.

5. The operating emissions quantification method of claim 1 , wherein the prediction model associated with the first air quality monitor is trained with the hPBL data continuously procured from the second server periodically at a second predetermined frequency, for at least 24 hours.

6. The operating emissions quantification method of claim 1 , wherein the first set of individual atmospheric readings comprises at least one of atmospheric reading selected from:

a wind speed,

a wind direction,

a barometric pressure,

an air temperature, and

a humidity level.

7. The operating emissions quantification method of claim 1 ,

wherein each feature group is associated with a wind-direction bucket; and

wherein a predetermined number of wind-direction buckets together are representative of wind-directions over a full circle.

8. The operating emissions quantification method of claim 7 , wherein generating the mapping further comprises:

comparing a plurality of first measured substance concentrations with the plurality of first predicted substance concentrations of the target substance to create at least an at least one adjustment factor;

obtaining from the prediction model, a wind-direction contribution value representative of a contribution of the wind-direction in each of the plurality of first predicted substance concentrations as predicted by the prediction model;

adjusting a plurality of wind-direction contribution values corresponding to the plurality of first predicted substance concentrations, using the at least one adjustment factor to obtain a plurality of adjusted wind-direction contribution values;

grouping the plurality of adjusted wind-direction contribution values into the predetermined number of wind-direction buckets;

for each of the predetermined number of wind-direction buckets, determining a weighted mean of the plurality of first predicted substance concentrations grouped in each wind-direction bucket of the predetermined number of wind-direction buckets; and

generating the mapping of the weighted mean of the plurality of first predicted substance concentrations grouped in each wind-direction bucket of the predetermined number of wind-direction buckets, for wind directions in a full circle.

9. A system for quantifying emissions of a target substance from operating devices at a site comprising:

one or more processors configured to:

provide a first air quality monitor comprising:

a first sensor responsive to the target substance; and

a first location at which the first air quality monitor is located on the site

measure a first set of onsite parameters with the first air quality monitor over a period of time to obtain a plurality of individual measurements of each parameter of the first set of onsite parameters, wherein the plurality of individual measurements of the first set of onsite parameters comprises:

a first measured substance concentration of the target substance measured with the first air quality monitor; and

a first set of individual atmospheric readings;

transmit the first measured substance concentration to a first server;

transmit the first set of individual atmospheric readings to the first server;

procure a height of planetary boundary layer (hPBL) data for the site from a second server;

train a prediction model associated with the first air quality monitor:

obtain a plurality of first predicted substance concentrations of the target substance corresponding to the first air quality monitor from the prediction model, with at least:

the first set of individual atmospheric readings; and

the hPBL data for the site;

wherein the plurality of first predicted substance concentrations is obtained over a predefined period at a predefined frequency;

generate a mapping of a weighted mean of the plurality of first predicted substance concentrations grouped in each feature group of a predetermined number of feature groups, wherein the predetermined number of feature groups together are representative of feature values over a predetermined range;

obtain a location map of a plurality of emission sources at the site, the location map comprising a location and an identity associated with each of the plurality of emission sources;

for each emission source of the plurality of emission sources:

generate a simulated plume model, based on a wind-direction; and

calculate a plurality of representative circular normal distributions for each air quality monitor, using the simulated plume model, by setting a plurality of presumed flux values to the simulated plume model;

perform an analysis of the plurality of representative circular normal distributions in relation with the mapping to identify a relevant representative circular normal distribution from the plurality of representative circular normal distributions, wherein the relevant representative circular normal distribution is indicative of a target emission source; and

determine the target emission source, based on the analysis and the location map.

10. The system of claim 9 , wherein the hPBL data further comprises:

a trapping condition corresponding to wind stagnation;

wherein during emission of the substance from an emission source, the trapping condition is associated with a gradual accumulation of the substance in an atmosphere surrounding the site and a higher first measured substance concentration of the target substance measured with the first air quality monitor.

11. The system of claim 9 , wherein the second server is High Resolution Rapid Refresh (HRRR) maintained by National Oceanic and Atmospheric Administration (NOAA).

12. The system of claim 11 , further comprising procuring the hPBL data from the second server periodically at a predetermined frequency;

wherein the hPBL data is refreshed on an hourly-basis to a resolution of 3 kilometers.

13. The system of claim 9 , wherein the prediction model associated with the first air quality monitor is trained with the hPBL data continuously procured from the second server periodically at a second predetermined frequency, for at least 24 hours.

14. The system of claim 9 , wherein the first set of individual atmospheric readings comprises at least one of atmospheric reading selected from:

a wind speed;

a wind direction;

a barometric pressure;

an air temperature, and

a humidity level.

15. The system of claim 9 , wherein each feature group is associated with a wind-direction bucket; and

wherein a predetermined number of wind-direction buckets together are representative of wind-directions over a full circle.

16. The system of claim 15 , wherein generating the mapping further comprises:

comparing a plurality of first measured substance concentrations with the plurality of first predicted substance concentrations of the target substance to create at least an at least one adjustment factor;

obtaining from the prediction model, a wind-direction contribution value representative of a contribution of the wind-direction in each of the plurality of first predicted substance concentrations as predicted by the prediction model;

adjusting a plurality of wind-direction contribution values corresponding to the plurality of first predicted substance concentrations, using the at least one adjustment factor to obtain a plurality of adjusted wind-direction contribution values;

grouping the plurality of adjusted wind-direction contribution values into the predetermined number of wind-direction buckets;

for each of the predetermined number of wind-direction buckets, determining a weighted mean of the plurality of first predicted substance concentrations grouped in each wind-direction bucket of the predetermined number of wind-direction buckets; and

generating the mapping of the weighted mean of the plurality of first predicted substance concentrations grouped in each wind-direction bucket of the predetermined number of wind-direction buckets, for wind directions in a full circle.