351.POLLUTION SOURCES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC REG | PROC SGPLOT | MACROS | DATE FUNCTIONS | APPEND | MERGE | TRANSPOSE

POLLUTION SOURCES DATA ANALYSIS USING SAS DATA STEP | PROC SQL | PROC MEANS | PROC REG | PROC SGPLOT | MACROS | DATE FUNCTIONS | APPEND | MERGE | TRANSPOSE 

options nocenter;

1.POLLUTION MAIN DATASET CREATION

data pollution_main;

    length Source_Type $20 City $15;

    format Report_Date date9.;

    input Source_Type $ City $ Volume Impact_Level Reduction_Cost Risk_Score 

          Report_Date : date9.;

    datalines;

Vehicle_Emissions Delhi 85 9 1200 88 01JAN2024

Industrial_Waste Mumbai 92 10 1800 94 15JAN2024

Construction_Dust Hyderabad 70 7 900 72 01FEB2024

Power_Plants Chennai 88 9 1600 90 20FEB2024

Waste_Burning Kolkata 76 8 1000 80 05MAR2024

Vehicular_Traffic Bengaluru 82 8 1300 85 18MAR2024

Chemical_Leak Pune 90 10 2000 96 01APR2024

Mining_Activity Ranchi 78 7 1100 75 10APR2024

Oil_Refinery Jamnagar 95 10 2200 98 25APR2024

Plastic_Waste Surat 65 6 700 60 05MAY2024

Thermal_Plant Korba 89 9 1700 92 15MAY2024

Brick_Kilns Patna 72 7 950 74 01JUN2024

Traffic_Congestion Noida 84 8 1250 86 10JUN2024

;

run;

proc print data=pollution_main;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score
1	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88
2	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72
4	Power_Plants	Chennai	20FEB2024	88	9	1600	90
5	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80
6	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85
7	Chemical_Leak	Pune	01APR2024	90	10	2000	96
8	Mining_Activity	Ranchi	10APR2024	78	7	1100	75
9	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98
10	Plastic_Waste	Surat	05MAY2024	65	6	700	60
11	Thermal_Plant	Korba	15MAY2024	89	9	1700	92
12	Brick_Kilns	Patna	01JUN2024	72	7	950	74
13	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86

2.DATE FUNCTION DERIVATIONS (INTNX, INTCK, MDY)

data pollution_dates;

    set pollution_main;

    Next_Report = intnx('month', Report_Date, 1, 'same');

    Days_Since_Report = intck('day', Report_Date, today());

    Year_Start = mdy(1,1,year(Report_Date));

    format Next_Report Year_Start date9.;

run;

proc print data=pollution_dates;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score	Next_Report	Days_Since_Report	Year_Start
1	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88	01FEB2024	726	01JAN2024
2	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94	15FEB2024	712	01JAN2024
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72	01MAR2024	695	01JAN2024
4	Power_Plants	Chennai	20FEB2024	88	9	1600	90	20MAR2024	676	01JAN2024
5	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80	05APR2024	662	01JAN2024
6	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85	18APR2024	649	01JAN2024
7	Chemical_Leak	Pune	01APR2024	90	10	2000	96	01MAY2024	635	01JAN2024
8	Mining_Activity	Ranchi	10APR2024	78	7	1100	75	10MAY2024	626	01JAN2024
9	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98	25MAY2024	611	01JAN2024
10	Plastic_Waste	Surat	05MAY2024	65	6	700	60	05JUN2024	601	01JAN2024
11	Thermal_Plant	Korba	15MAY2024	89	9	1700	92	15JUN2024	591	01JAN2024
12	Brick_Kilns	Patna	01JUN2024	72	7	950	74	01JUL2024	574	01JAN2024
13	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86	10JUL2024	565	01JAN2024

3.PROC SQL – SUMMARY AND FILTERING

proc sql;

    create table high_risk_sources as

    select City,Source_Type,Volume,Risk_Score

    from pollution_main

    where Risk_Score >= 85

    order by Risk_Score desc;

quit;

proc print data=high_risk_sources;

run;

OUTPUT:

Obs	City	Source_Type	Volume	Risk_Score
1	Jamnagar	Oil_Refinery	95	98
2	Pune	Chemical_Leak	90	96
3	Mumbai	Industrial_Waste	92	94
4	Korba	Thermal_Plant	89	92
5	Chennai	Power_Plants	88	90
6	Delhi	Vehicle_Emissions	85	88
7	Noida	Traffic_Congestion	84	86
8	Bengaluru	Vehicular_Traffic	82	85

4.PROC MEANS – STATISTICAL SUMMARY

proc means data=pollution_main mean min max;

    var Volume Reduction_Cost Risk_Score;

run;

OUTPUT:

The MEANS Procedure

Variable	Mean	Minimum	Maximum
Volume Reduction_Cost Risk_Score	82.0000000 1361.54 83.8461538	65.0000000 700.0000000 60.0000000	95.0000000 2200.00 98.0000000

5.MACRO FOR SEVERITY CLASSIFICATION

%macro classify_severity;

data pollution_severity;

    set pollution_main;

    length Severity $12;

    if Risk_Score >= 90 then Severity = 'CRITICAL';

    else if Risk_Score >= 75 then Severity = 'HIGH';

    else if Risk_Score >= 60 then Severity = 'MODERATE';

    else Severity = 'LOW';

run;

proc print data=pollution_severity;

run;

%mend;

%classify_severity;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score	Severity
1	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88	HIGH
2	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94	CRITICAL
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72	MODERATE
4	Power_Plants	Chennai	20FEB2024	88	9	1600	90	CRITICAL
5	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80	HIGH
6	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85	HIGH
7	Chemical_Leak	Pune	01APR2024	90	10	2000	96	CRITICAL
8	Mining_Activity	Ranchi	10APR2024	78	7	1100	75	HIGH
9	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98	CRITICAL
10	Plastic_Waste	Surat	05MAY2024	65	6	700	60	MODERATE
11	Thermal_Plant	Korba	15MAY2024	89	9	1700	92	CRITICAL
12	Brick_Kilns	Patna	01JUN2024	72	7	950	74	MODERATE
13	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86	HIGH

6.PROC REG – RELATIONSHIP ANALYSIS

proc reg data=pollution_main;

    model Risk_Score = Volume Reduction_Cost;

run;

quit;

OUTPUT:

The REG Procedure

Model: MODEL1

Dependent Variable: Risk_Score

Number of Observations Read	13
Number of Observations Used	13

Analysis of Variance
Source	DF	Sum of Squares	Mean Square	F Value	Pr > F
Model	2	1412.72842	706.36421	108.73	<.0001
Error	10	64.96388	6.49639
Corrected Total	12	1477.69231

Root MSE	2.54880	R-Square	0.9560
Dependent Mean	83.84615	Adj R-Sq	0.9472
Coeff Var	3.03985

Parameter Estimates
Variable	DF	Parameter Estimate	Standard Error	t Value	Pr > |t|
Intercept	1	-11.25012	13.19922	-0.85	0.4140
Volume	1	1.14905	0.23030	4.99	0.0005
Reduction_Cost	1	0.00064217	0.00461	0.14	0.8920

The REG Procedure

Model: MODEL1

Dependent Variable: Risk_Score

7.PROC SGPLOT – VISUALIZATION

proc sgplot data=pollution_main;

    scatter x=Volume y=Risk_Score / group=Source_Type;

    reg x=Volume y=Risk_Score;

    title "Pollution Volume vs Risk Score";

run;

OUTPUT:

8.PROC APPEND – ADDITIONAL DATA

data pollution_new;

    length Source_Type $20 City $15;

    format Report_Date date9.;

    Source_Type='E_Waste';

    City='Gurgaon';

    Volume=68;

    Impact_Level=6;

    Reduction_Cost=800;

    Risk_Score=65;

    Report_Date='20JUN2024'd;

run;

proc print data=pollution_new;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score
1	E_Waste	Gurgaon	20JUN2024	68	6	800	65

proc append base=pollution_main 

            data=pollution_new;

run;

proc print data=pollution_main;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score
1	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88
2	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72
4	Power_Plants	Chennai	20FEB2024	88	9	1600	90
5	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80
6	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85
7	Chemical_Leak	Pune	01APR2024	90	10	2000	96
8	Mining_Activity	Ranchi	10APR2024	78	7	1100	75
9	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98
10	Plastic_Waste	Surat	05MAY2024	65	6	700	60
11	Thermal_Plant	Korba	15MAY2024	89	9	1700	92
12	Brick_Kilns	Patna	01JUN2024	72	7	950	74
13	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86
14	E_Waste	Gurgaon	20JUN2024	68	6	800	65

9.PROC MERGE – COST CATEGORY DATA

data cost_category;

    length Source_Type $20 Cost_Class $10;

    input Source_Type $ Cost_Class $;

    datalines;

Vehicle_Emissions HIGH

Industrial_Waste HIGH

Construction_Dust MEDIUM

Waste_Burning MEDIUM

Plastic_Waste LOW

;

run;

proc print data=cost_category;

run;

OUTPUT:

Obs	Source_Type	Cost_Class
1	Vehicle_Emissions	HIGH
2	Industrial_Waste	HIGH
3	Construction_Dust	MEDIUM
4	Waste_Burning	MEDIUM
5	Plastic_Waste	LOW

proc sort data=pollution_main; by Source_Type; run;

proc print data=pollution_main;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score
1	Brick_Kilns	Patna	01JUN2024	72	7	950	74
2	Chemical_Leak	Pune	01APR2024	90	10	2000	96
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72
4	E_Waste	Gurgaon	20JUN2024	68	6	800	65
5	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94
6	Mining_Activity	Ranchi	10APR2024	78	7	1100	75
7	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98
8	Plastic_Waste	Surat	05MAY2024	65	6	700	60
9	Power_Plants	Chennai	20FEB2024	88	9	1600	90
10	Thermal_Plant	Korba	15MAY2024	89	9	1700	92
11	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86
12	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88
13	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85
14	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80

proc sort data=cost_category; by Source_Type; run;

proc print data=cost_category;

run;

OUTPUT:

Obs	Source_Type	Cost_Class
1	Construction_Dust	MEDIUM
2	Industrial_Waste	HIGH
3	Plastic_Waste	LOW
4	Vehicle_Emissions	HIGH
5	Waste_Burning	MEDIUM

data pollution_merged;

    merge pollution_main(in=a) cost_category(in=b);

    by Source_Type;

    if a;

run;

proc print data=pollution_merged;

run;

OUTPUT:

Obs	Source_Type	City	Report_Date	Volume	Impact_Level	Reduction_Cost	Risk_Score	Cost_Class
1	Brick_Kilns	Patna	01JUN2024	72	7	950	74
2	Chemical_Leak	Pune	01APR2024	90	10	2000	96
3	Construction_Dust	Hyderabad	01FEB2024	70	7	900	72	MEDIUM
4	E_Waste	Gurgaon	20JUN2024	68	6	800	65
5	Industrial_Waste	Mumbai	15JAN2024	92	10	1800	94	HIGH
6	Mining_Activity	Ranchi	10APR2024	78	7	1100	75
7	Oil_Refinery	Jamnagar	25APR2024	95	10	2200	98
8	Plastic_Waste	Surat	05MAY2024	65	6	700	60	LOW
9	Power_Plants	Chennai	20FEB2024	88	9	1600	90
10	Thermal_Plant	Korba	15MAY2024	89	9	1700	92
11	Traffic_Congestion	Noida	10JUN2024	84	8	1250	86
12	Vehicle_Emissions	Delhi	01JAN2024	85	9	1200	88	HIGH
13	Vehicular_Traffic	Bengaluru	18MAR2024	82	8	1300	85
14	Waste_Burning	Kolkata	05MAR2024	76	8	1000	80	MEDIUM

10.PROC TRANSPOSE – CITY-WISE VIEW

proc transpose data=pollution_main out=city_volume prefix=Volume_;

    by City NotSorted;

    var Volume;

run;

proc print data=city_volume;

run;

OUTPUT:

Obs	City	_NAME_	Volume_1
1	Patna	Volume	72
2	Pune	Volume	90
3	Hyderabad	Volume	70
4	Gurgaon	Volume	68
5	Mumbai	Volume	92
6	Ranchi	Volume	78
7	Jamnagar	Volume	95
8	Surat	Volume	65
9	Chennai	Volume	88
10	Korba	Volume	89
11	Noida	Volume	84
12	Delhi	Volume	85
13	Bengaluru	Volume	82
14	Kolkata	Volume	76

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