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Environmental Data Analytics
Dr Prasad Modak, Tausif Farooqui
1
Structure of Presentation
2
EMC’s Experiences
Environmental Data Analytics
3
EMC
Experiences on
Environmental
Data analytics
• Dr Prasad Modak had setup the first data base
management system in India...
EMC
Experiences on
Environmental
Data analytics
Indian
Regulators
International
Financing
institution
Manufacturing
Indust...
EMC
Experiences on
Environmental
Data analytics
• Industries are responsible to collect data for mandatory
submission to r...
EMC
Experiences on
Environmental
Data analytics
• Egyptian Pollution Abatement Programme-phase 2
• Dr Prasad Modak had set...
EMC
Experiences on
Environmental
Data analytics
• Analysis of real-time water quality monitoring data of River
Ganga – 201...
EMC
Experiences on
Environmental
Data analytics
• Analysis of air quality in a air shed with 2 power plants -
2016
• Data ...
EMC
Experiences on
Environmental
Data analytics
• EMC had setup an EMIS for Rourkela steel plant in 2004
Indian
Regulators...
EMC
Experiences on
Environmental
Data analytics
• Corporate Sustainability Report for Glenmark
Pharmaceuticals -2015
India...
EMC
Experiences on
Environmental
Data analytics
• Web based GIS for management for the Ahmedabad-
Mehsana Toll Road.
• Dat...
EMC
Experiences on
Environmental
Data analytics
• Designing the data structure
• Designing the MIS, EMIS
• Online/offline ...
Structure of Presentation
14
Characteristics of
Environmental Data
Environmental Data Analytics
15
Characteristics
of
Environmental
Data
Multivariate
Environmental
Data
Stack Emission
Ambient Air
Quality
Meteorological
Me...
Characteristics of
Environmental
Data
BIG DATA
• Summing up all the characteristics of environmental data
• Essentially a ...
Characteristics of
Environmental
Data
• Various data sets have different frequency of
measurements
• Ambient Air Quality p...
Characteristics of
Environmental
Data
Data from different types of instrumentation
Multivariate BIG Data Irregular Fuzzy
19
Characteristics of
Environmental
Data
• Large volumes of data
• Uncertainties are very high
• Frequent and large Data gaps...
To derive value from these data sets, advanced
application of DATA analytics is required
21
Role Based
reports
Characteristics of
Environmental
Data
Raw Data –Site Engineer,
Developer
Sub indices – Line
Manager
Ind...
Structure of Presentation
23
Dashboard
Environmental Data Analytics
24
Dashboard
• Visual representation of the Data
• All the information at one place
• Key Indicators
• A single screen for in...
Dashboard
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
26
Dashboard
• Do you record data or measurements ? Yes
• Where do you use the recorded data ?
• For decision making
• For co...
Dashboard
BIG DATA Analytics
• To understand the data
• To manage the data
• To process the captured data in a hierarchica...
Dashboard
http://www.forbes.com/
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
29
Dashboard
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
30
Dashboard
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
31
Dashboard
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
Dedicated Management
Information System - MI...
Dashboard
What is a
Dashboard?
Why
Dashboard?
Sample
Dashboards
Tools Tableau
Analytics and
Business
Intelligence tools
SA...
Dashboard
• Tableau is a business intelligence (BI) tool
• Tableau connects easily to nearly any data source
• Tableau all...
Dashboard
• Tableau's Data Analytic and Visualization Features
Interactive visualizations
• Simultaneous Connections with ...
Structure of Presentation
36
Dashboard for Air Quality
Management
Environmental Data Analytics
37
What does Real
Time
Environmental
Data Analytics
Involve?
Dashboard
for Air Quality
Management
• Allows Quick and Dynamic ...
• Emissions
• Ambient Air Quality
• Meteorology
• Ambient, Stack and Automatic monitoring and
metrological data in a singl...
Analytical
features for
Ambient Air
Dashboard for
Air Quality
Management
• Missing Values treatment
• Outliers
• Basic Sta...
• Time – Date of observation/ measurement
• Observed/Measured values of
• Concentration of Particulate Matter (PM10, PM2.5...
Analytical
features for
Stack Emission
Dashboard for
Air Quality
Management
• Missing Values
• Outliers
• Basic Statistics...
Parameters for
Stack Emission
Dashboard
for Air Quality
Management
• Time – Data of observation/ measurement
• Observed/Me...
Analytical
features for
Metrological
data
Dashboard for
Air Quality
Management
• Wind rose,
• Pollution rose
• Persistent ...
Parameters for
Meteorological
data
Dashboard for
Air Quality
Management
• Time – Data of observation/ measurement
• Observ...
Missing Value
Dashboard for
Air Quality
Management
• an ‘observation not recorded’
• Treatment of Missing Value
• Replaced...
Demo Dashboard
Visualize parameters
1. NOx
2. SOx
3. PM10
4. PM2.5
Can be extended to
include other
parameters
Demo is pre...
Options in dropdown boxes
Select Visualization Type
– Maps, Statistics, Charts
Map
Chart
Stats
Dashboard for
Air Quality
M...
Dashboard for
Air Quality
Management
User can only
download the PDF
and Image of the
screen.
User cannot
however
download ...
Station 1 Station 2Parameter 1 Parameter 2
AveragingPeriod
User options
Dashboard
for Air Quality
Management
Features Inpu...
Selected stations are located on
the map,
Map is sourced from ISRO-
Bhuvan
Click to open satellite view of the
location in...
Green band – permitted
values in designated water
quality class
Black lines – higher and lower
Statistically acceptable
pe...
Basic Statistics
Number of Records
Max, Min, Mean, Median
SD, CoV, Number of Outliers
Percentage violations, and
Longest C...
• An outlier is an observation
point that is distant from
other observations.
• User option to select
method of treating o...
Basic Statistics
Box Whisker
Plots
Dashboard for
Air Quality
Management
Annual Maximum
Annual Minimum
Annual Mean
CPCB sta...
Compliance
with standards
with
timestamps
Dashboard for
Air Quality
Management
Dates of violations
Features Input
Paramete...
• Radial Chart
• Distribution of Wind speeds Vs Direction
Wind Rose
Dashboard for
Air Quality
Management
Features Input
Pa...
Inter-station
correlations
Dashboard for
Air Quality
Management
Features Input
Parameters
illustrations New
Concepts
The F...
Trend Analysis
(magnitude,
direction and
statistical
significance)
Dashboard for
Air Quality
Management
Features Input
Par...
• Radial Chart
• Distribution of Pollutant Concentration Vs Direction
Pollution Rose
Dashboard for
Air Quality
Management
...
Compliance
Histogram
Dashboard for
Air Quality
Management
Features Input
Parameters
illustrations New
Concepts
The Future
...
Detecting
simultaneous
Violations
Dashboard for
Air Quality
Management
Features Input
Parameters
illustrations New
Concept...
• Percent exceedance (how
many times is the standard
for a parameter violated in
relation to the length of the
data chain)...
Location
Importance
Index (LII)
Dashboard for
Air Quality
Management
Locations could be a receptor, ambient station or a s...
• Distribution of Consistency in wind direction Vs Direction in the number of
hours in a Radial Chart
• Data Quality check...
Stack-station
Influence
analysis
Dashboard for
Air Quality
Management
• Influence of emissions
from a particular stack
on ...
Stack-station
Influence
analysis
Dashboard for
Air Quality
Management
Air Quality measurements are
taken at Stack and Ambi...
Non-
Parametric
Wind
Regression
(NWR) for
Source
Identification
Dashboard for
Air Quality
Management
• NWR has been applie...
Non-
Parametric
Wind
Regression
(NWR) for
Source
Identification
Dashboard for
Air Quality
Management
Features Input
Parame...
Source
Diagnostics or
Environmental
Forensics
Dashboard for
Air Quality
Management
• Data science + Environmental science
...
AERMOD
Air Dispersion
Modelling
71
• US EPA based Model
• Gaussian plume air dispersion model
• Predict downwind pollutant...
Source
apportionment
Air Dispersion
Modelling
72
Features Input
Parameters
illustrations New
Concepts
The Future
Click on ...
Modelled percentage contribution of two power plants
of ambient PM10 at a sensitive receptor
Period % contribution of A % ...
Integration of
Models and
Dashboard
Integrated
Approach
74
• Air Emission and Air Quality Dashboard integrated with
the Di...
PEMS
Predictive
Emissions
Monitoring
System
75
Features Input
Parameters
illustrations New
Concepts
The Future
PEMS
Predictive
Emissions
Monitoring
System
76
• Developed on Artificial Neural
Network (ANN) concepts
• The model automat...
CEMS PEMS
Cost Large capital & operational expenditure Less expensive (50% to 75%) than CEMS.
Maintenance Periodic mainten...
Thank you
Please visit
http://dev-data-portal.pantheonsite.io/
Dr Prasad Modak – Prasad.modak@emcentre.com
Tausif Farooqui...
Structure of Presentation
79
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Environmental Data Management and Analytics

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Environmental Data Management and Analytics
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Environmental Data Management and Analytics

  1. 1. Environmental Data Analytics Dr Prasad Modak, Tausif Farooqui 1
  2. 2. Structure of Presentation 2
  3. 3. EMC’s Experiences Environmental Data Analytics 3
  4. 4. EMC Experiences on Environmental Data analytics • Dr Prasad Modak had setup the first data base management system in India for environmental data for CPCB in 1986 Which included • Air Data management • Water data management, • Consent managements • Cess calculations • Water Quality data analysis for Godavari • Monthly data for 7 years Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 4
  5. 5. EMC Experiences on Environmental Data analytics Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 5
  6. 6. EMC Experiences on Environmental Data analytics • Industries are responsible to collect data for mandatory submission to regulator, by law. • Industries also use these data sets for disclosures • And in sustainability reports Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 6
  7. 7. EMC Experiences on Environmental Data analytics • Egyptian Pollution Abatement Programme-phase 2 • Dr Prasad Modak had setup an online environmental monitoring system which was to serve regulator and financer Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 7
  8. 8. EMC Experiences on Environmental Data analytics • Analysis of real-time water quality monitoring data of River Ganga – 2015 • Data generated by sensors installed at 10 stations. • Geo-Database for Punjab water supply department – 2016 • Analysis and Dashboard for National Ground Water Management Improvement program- India 2016 Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 8
  9. 9. EMC Experiences on Environmental Data analytics • Analysis of air quality in a air shed with 2 power plants - 2016 • Data from Ambient monitoring, stack measurements, automatic real-time monitoring, and metrological real time monitoring were analyzed • Air quality model was built for spatial prediction of pollutant concentration Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 9
  10. 10. EMC Experiences on Environmental Data analytics • EMC had setup an EMIS for Rourkela steel plant in 2004 Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 10
  11. 11. EMC Experiences on Environmental Data analytics • Corporate Sustainability Report for Glenmark Pharmaceuticals -2015 Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 11
  12. 12. EMC Experiences on Environmental Data analytics • Web based GIS for management for the Ahmedabad- Mehsana Toll Road. • Data of Maintenance and toll • Cause-effect analysis for accidents • Showcased by ESRI an one of the first application of ARC on the web Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 12
  13. 13. EMC Experiences on Environmental Data analytics • Designing the data structure • Designing the MIS, EMIS • Online/offline applications • Environmental Data analysis • Environmental modeling • GIS applications • Community applications – crowd sourcing Indian Regulators International Financing institution Manufacturing Industries Infrastructure and Transport Industry Our Strengths 13
  14. 14. Structure of Presentation 14
  15. 15. Characteristics of Environmental Data Environmental Data Analytics 15
  16. 16. Characteristics of Environmental Data Multivariate Environmental Data Stack Emission Ambient Air Quality Meteorological Measurements Health Data •Patients, Diseases, Mortality… Vegetation Data •Bio Diversity, Green Cover… Agricultural Data •Productivity, Yield.. BIG Data Irregular Fuzzy Primary Data and Meta Data 16
  17. 17. Characteristics of Environmental Data BIG DATA • Summing up all the characteristics of environmental data • Essentially a BIG DATA management and processing system is needed • To generate meaningful and actionable reports from the data Multivariate BIG Data Irregular Fuzzy 17
  18. 18. Characteristics of Environmental Data • Various data sets have different frequency of measurements • Ambient Air Quality parameters are monitored twice a week while stack and metrological data is monitored in real time (15 min period). • Other data sets like heath, agriculture are recorded and published monthly or seasonally. • It creates a complicated situation to merge and consolidate all data sets. Multivariate BIG Data Irregular Fuzzy 18
  19. 19. Characteristics of Environmental Data Data from different types of instrumentation Multivariate BIG Data Irregular Fuzzy 19
  20. 20. Characteristics of Environmental Data • Large volumes of data • Uncertainties are very high • Frequent and large Data gaps • Accuracy of measurement is questionable at some instances • It makes the data not ready for consumption of decision makers Multivariate BIG Data Irregular Fuzzy 20
  21. 21. To derive value from these data sets, advanced application of DATA analytics is required 21
  22. 22. Role Based reports Characteristics of Environmental Data Raw Data –Site Engineer, Developer Sub indices – Line Manager Indicators – Department Managers Dashboard – CEO, CFO, CxOs 22
  23. 23. Structure of Presentation 23
  24. 24. Dashboard Environmental Data Analytics 24
  25. 25. Dashboard • Visual representation of the Data • All the information at one place • Key Indicators • A single screen for insights and analytics • Objective oriented or goal based • Advanced statistical/mathematical models What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 25
  26. 26. Dashboard What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 26
  27. 27. Dashboard • Do you record data or measurements ? Yes • Where do you use the recorded data ? • For decision making • For compliance • Information dissemination – mass communication • How do you use the recorded data ? • Print ? • Excel, pivot tables ? • Or Visualize ? • For BIG data which method would you prefer? What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 27
  28. 28. Dashboard BIG DATA Analytics • To understand the data • To manage the data • To process the captured data in a hierarchical manner • To visually communicate data • To generate Role based reports instantly • To make data driven business or operations decisions • To create alerts 28 What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau
  29. 29. Dashboard http://www.forbes.com/ What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 29
  30. 30. Dashboard What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 30
  31. 31. Dashboard What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 31
  32. 32. Dashboard What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau Dedicated Management Information System - MIS Environmental Management Information System - EMIS Analytics and Business Intelligence tools 32
  33. 33. Dashboard What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau Analytics and Business Intelligence tools SAS Qlikview Tableau And many more 33
  34. 34. Dashboard • Tableau is a business intelligence (BI) tool • Tableau connects easily to nearly any data source • Tableau allows transforming data into visually appealing, interactive visualizations • Compatibility across Multiple Platforms - desktop tool, web browser, iPad or mobile phone. What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 34
  35. 35. Dashboard • Tableau's Data Analytic and Visualization Features Interactive visualizations • Simultaneous Connections with multiple data sources • Allows to create custom calculations • Charts, Graphs, Heat maps etc. • Time series functions • Map visualizations (point, line, polygon) • Access control • Multiple simultaneous dashboards What is a Dashboard? Why Dashboard? Sample Dashboards Tools Tableau 35
  36. 36. Structure of Presentation 36
  37. 37. Dashboard for Air Quality Management Environmental Data Analytics 37
  38. 38. What does Real Time Environmental Data Analytics Involve? Dashboard for Air Quality Management • Allows Quick and Dynamic Visualizations • Mines the BIG data to Optimize Sampling Frequencies and get More Value • Uses Real Time Algorithms with Advanced Statistical Tools • Detects Trends, Patterns and allows Forecasting • Helps in establishing association between Emissions and Ambient Quality or understand Source Influences Features Input Parameters illustrations New Concepts The Future 38
  39. 39. • Emissions • Ambient Air Quality • Meteorology • Ambient, Stack and Automatic monitoring and metrological data in a single dashboard for effective interpretation, reporting and taking action What are the important factors for air quality management? Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 39
  40. 40. Analytical features for Ambient Air Dashboard for Air Quality Management • Missing Values treatment • Outliers • Basic Statistics (Mean, Deviations, Percentiles, Type of Distribution) • Box Whisker Plots • Histograms and Frequency Diagrams • Compliance with standards with timestamps • Detecting simultaneous Violations • Location Importance Index • Correlations – Inter parameter and Inter station • Trend Analysis (magnitude, direction and statistical significance) • Air Quality Index Features Input Parameters illustrations New Concepts The Future 40
  41. 41. • Time – Date of observation/ measurement • Observed/Measured values of • Concentration of Particulate Matter (PM10, PM2.5) • Concentration of Gasses ( CO2, NOx ) • Location of the station • Meta data like up time, calibration date, battery level, sensor make, sensor accuracy etc.,. Parameters for Ambient Air Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 41
  42. 42. Analytical features for Stack Emission Dashboard for Air Quality Management • Missing Values • Outliers • Basic Statistics (Mean, Deviations, Percentiles, Type of Distribution) • Box Whisker Plots • Histograms and Frequency Diagrams • Compliance with standards with timestamps • Detecting simultaneous Violations • Stack Importance Index • Predictive Emission Modelling Features Input Parameters illustrations New Concepts The Future 42
  43. 43. Parameters for Stack Emission Dashboard for Air Quality Management • Time – Data of observation/ measurement • Observed/Measured values of • Concentration of SPM, other pollutants • Temperature • Generation • Location of the stack • Meta data like up time, calibration date, battery level, sensor make, sensor accuracy etc.,. Features Input Parameters illustrations New Concepts The Future 43
  44. 44. Analytical features for Metrological data Dashboard for Air Quality Management • Wind rose, • Pollution rose • Persistent wind rose • Non-Parametric Wind Regression (NWR) for Source Identification • Stack-station Influence analysis Features Input Parameters illustrations New Concepts The Future 44
  45. 45. Parameters for Meteorological data Dashboard for Air Quality Management • Time – Data of observation/ measurement • Observed/Measured values of • Temperature • Wind Speed • Wind Direction • Relative Humidity • Solar Radiation Features Input Parameters illustrations New Concepts The Future 45
  46. 46. Missing Value Dashboard for Air Quality Management • an ‘observation not recorded’ • Treatment of Missing Value • Replaced by Mean of Window or the selected time span • Replaced by Mean of Neighbors Features Input Parameters illustrations New Concepts The Future 46
  47. 47. Demo Dashboard Visualize parameters 1. NOx 2. SOx 3. PM10 4. PM2.5 Can be extended to include other parameters Demo is prepared for AIR, a similar dashboard can be developed for any other Environmental Data Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 47
  48. 48. Options in dropdown boxes Select Visualization Type – Maps, Statistics, Charts Map Chart Stats Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 48
  49. 49. Dashboard for Air Quality Management User can only download the PDF and Image of the screen. User cannot however download the raw data behind it. Features Input Parameters illustrations New Concepts The Future 49
  50. 50. Station 1 Station 2Parameter 1 Parameter 2 AveragingPeriod User options Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 50
  51. 51. Selected stations are located on the map, Map is sourced from ISRO- Bhuvan Click to open satellite view of the location in Google maps Map Visualization Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 51
  52. 52. Green band – permitted values in designated water quality class Black lines – higher and lower Statistically acceptable permitted values, beyond this value is an outlier Blue line – Chart of selected parameter Red dots – Both of the selected parameters are violating applicable standards simultaneously Stations Parameter 1 Parameter 2 - Charts Visualization Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 52
  53. 53. Basic Statistics Number of Records Max, Min, Mean, Median SD, CoV, Number of Outliers Percentage violations, and Longest Contiguous violation for both the selected parameters StationsParameter 1 Parameter 2 Percentage simultaneous violations by both parameters against relevant standard. Longest Contiguous and Simultaneous violation Crosstab Visualization Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 53
  54. 54. • An outlier is an observation point that is distant from other observations. • User option to select method of treating outliers • User option: Show/Hide outlier • Outlier is removed and treated as missing value Outlier Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 54
  55. 55. Basic Statistics Box Whisker Plots Dashboard for Air Quality Management Annual Maximum Annual Minimum Annual Mean CPCB standard Standard Deviation Features Input Parameters illustrations New Concepts The Future 55
  56. 56. Compliance with standards with timestamps Dashboard for Air Quality Management Dates of violations Features Input Parameters illustrations New Concepts The Future 56
  57. 57. • Radial Chart • Distribution of Wind speeds Vs Direction Wind Rose Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 57
  58. 58. Inter-station correlations Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 58
  59. 59. Trend Analysis (magnitude, direction and statistical significance) Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 59
  60. 60. • Radial Chart • Distribution of Pollutant Concentration Vs Direction Pollution Rose Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 60
  61. 61. Compliance Histogram Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 61
  62. 62. Detecting simultaneous Violations Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 62
  63. 63. • Percent exceedance (how many times is the standard for a parameter violated in relation to the length of the data chain) • Extent of contiguous exceedance (how long have been the violation over standard on a continuous basis in relation to the length of the data chain) • Magnitude of exceedance (how high are the violations when there is an exceedance over standard, expressed in sum of squares of deviations) Location Importance Index (LII) Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 63
  64. 64. Location Importance Index (LII) Dashboard for Air Quality Management Locations could be a receptor, ambient station or a stack Features Input Parameters illustrations New Concepts The Future 64
  65. 65. • Distribution of Consistency in wind direction Vs Direction in the number of hours in a Radial Chart • Data Quality check for automatic metereology stations 0 2 4 6 8 N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW 2014 Persistent wind rose Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 65 0 50 100 150 200 250 N NNE NE ENE E ESE SE SSE S SSW SW WSW W WNW NW NNW 2012
  66. 66. Stack-station Influence analysis Dashboard for Air Quality Management • Influence of emissions from a particular stack on a particular ambient station • Instances of influence are plotted as time- series Features Input Parameters illustrations New Concepts The Future 66
  67. 67. Stack-station Influence analysis Dashboard for Air Quality Management Air Quality measurements are taken at Stack and Ambient Estimates influence of a stack emission on a specific ambient monitoring station Longest contiguous duration of a receptor under influence of stack Features Input Parameters illustrations New Concepts The Future 67
  68. 68. Non- Parametric Wind Regression (NWR) for Source Identification Dashboard for Air Quality Management • NWR has been applied for better understanding of emission sources of influence since early 2000 • Technique of NWR is used to identify presence and locations of major emission influence on the observed pollutant concentrations • NWR requires high frequency (e.g. one hour) concentration and meteorological data at the location where emission influences are to be assessed Features Input Parameters illustrations New Concepts The Future 68
  69. 69. Non- Parametric Wind Regression (NWR) for Source Identification Dashboard for Air Quality Management Features Input Parameters illustrations New Concepts The Future 69
  70. 70. Source Diagnostics or Environmental Forensics Dashboard for Air Quality Management • Data science + Environmental science • Application of Sophisticated modelling tools, AERMOD for air quality • Receptor Modelling • Source identification using Met and Pollution Concentration Data • Analyzing of special events – Technique of Intervention analyses • Predictive emissions modelling system Features Input Parameters illustrations New Concepts The Future 70
  71. 71. AERMOD Air Dispersion Modelling 71 • US EPA based Model • Gaussian plume air dispersion model • Predict downwind pollutant concentrations based on source emissions, meteorological field, and site parameters (land use, terrain features etc.) • Model can predict accurately for 50km Features Input Parameters illustrations New Concepts The Future
  72. 72. Source apportionment Air Dispersion Modelling 72 Features Input Parameters illustrations New Concepts The Future Click on the box to see Percentage contribution of two power plants of ambient PM10 at a sensitive receptor
  73. 73. Modelled percentage contribution of two power plants of ambient PM10 at a sensitive receptor Period % contribution of A % contribution of B Winter 36.1 4.7 Summer 14.3 13.9 Post monsoon 45.3 7.9 Annual 32.2 8.5 73
  74. 74. Integration of Models and Dashboard Integrated Approach 74 • Air Emission and Air Quality Dashboard integrated with the Dispersion models • Model could be “fired” from the dashboard itself and required data on emissions, air quality and meteorology will be picked up automatically from data available. • Model will keep re-calibrating itself with actual real time data and this will increase the accuracy of the model outputs. • Models will also help to “validate” the data collected, thereby improving the quality of monitored data. Features Input Parameters illustrations New Concepts The Future
  75. 75. PEMS Predictive Emissions Monitoring System 75 Features Input Parameters illustrations New Concepts The Future
  76. 76. PEMS Predictive Emissions Monitoring System 76 • Developed on Artificial Neural Network (ANN) concepts • The model automatically predict emissions data in real time while exploiting the inherent correlation between process variables (flow, temperature and pressure) and emission properties (NOx, SO2, CO, CO2) • PEMS can replace CEMS and hence save on costs Features Input Parameters illustrations New Concepts The Future Click on the box to see Comparison between CEMS & PEMS
  77. 77. CEMS PEMS Cost Large capital & operational expenditure Less expensive (50% to 75%) than CEMS. Maintenance Periodic maintenance and calibration of the sensor and analyser is required No additional instrument maintenance (other than normal process sensors) is required Accuracy CEMS are the standard to which PEMS are measured PEMS are initially calibrated using CEMS. The CEMS are removed when the PEMS becomes operational. PEMS are less accurate than CEMS when the latter is well maintained and calibrated Uses CEMS alarms when emissions are approaching or exceeding an environmental standard PEMS can alarm, but also predict future outcomes and optimize processes in real-time. PEMS therefore leads to higher production efficiency and hence lower cost of production. 77 Comparison of CEMS and PEMS
  78. 78. Thank you Please visit http://dev-data-portal.pantheonsite.io/ Dr Prasad Modak – Prasad.modak@emcentre.com Tausif Farooqui – Tausif.farooqui@emcentre.com 78
  79. 79. Structure of Presentation 79

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