#ID: ID_BEI_DC
 #City: Beijing
 #Station: Dongcheng
 #Name: 东城东四
 #Latitude: 39.929
 #Longitude: 116.417
 #Timezone: +0800
 Date,PM10,PM25,CO,Ozone,Sulphur Dioxide,Nitrogen Dioxide,AmbientTemperature,RelativeHumidity,WindDirection,WindSpeed,Pressure,RainGauge
 Unit,ug/m3,ug/m3,ppm,µg/m3,µg/m3,µg/m3,°C,%,°,m/s,hPa,mm
 10/29/2016 13:00,16,3,,58,10,3,32,66,200,3,1001,0
 10/29/2016 14:00,19,8,,57,9,4,32,64,197,2,1001,0
 10/29/2016 15:00,15,9,,52,47,17,30,72,190,2,1001,0
 10/29/2016 16:00,31,19,,52,34,17,30,75,191,2,1001,0
 10/29/2016 17:00,31,17,,49,49,19,29,75,194,1,1002,0
 10/29/2016 18:00,37,18,,45,55,25,29,73,183,1,1003,0
 10/29/2016 19:00,24,13,,40,21,19,29,80,65,1,1004,0
 10/29/2016 20:00,39,22,,44,4,16,28,85,7,1,1005,0
 10/29/2016 21:00,24,16,,43,3,7,28,85,10,1,1005,0
 

This is an example based on an HTML format (direct url: publishingdata/html/):

This is an example based on JSON format (direct url: /publishingdata/json/)

For instant particule counters, such as the Plantower PMS or Nova SDS, which provide instantaneous (in few seconds) particulate matter readings, the data is usually reported every minute. This data is usually computed based on several "averaged" readings from a one or two minute period.

The use of averaging is however not good enough, especially for failing sensors (or sensor close of end-of-life). Therefore, for such sensors, it is required to provide additonal metrics, such as the median, min, max, and standard-deviation. See for example the readings object in the below JSON data feed.

..

You can use the following arduino-compatible code to collect those metrics:

class Accumulator 
{ 
    #define OUTPUT_BUFFER_SIZE 120 
    char buffer[OUTPUT_BUFFER_SIZE]; 
 
    #define MAXACCVALUES 120 
    int vals[MAXACCVALUES]; 
 
    int count = 0; 
 
public: 
    Accumulator() 
    { 
        reset(); 
    } 
 
    void reset() 
    { 
        count = 0; 
        for (int i=0;i< MAXACCVALUES;i++)  
        { 
            vals[i]=0; 
        } 
    } 
 
    bool hasData()  
    { 
        return count!=0; 
    } 
 
    std::string output()  
    { 
        if (!hasData()) return std::string("{}"); 
 
        sprintf(buffer, OUTPUT_BUFFER_SIZE, "{\"min\":%d,\"max\":%d,\"median\":%d,\"average\":%.1f,\"stddev\":%.1f,\"count\":%d}",  
            vmin(), vmax(), median(), average(), stddev(), count); 
 
        return std::string(buffer); 
 
    } 
 
    void add(int val) 
    { 
        if (count==MAXACCVALUES-1) { 
          for (int i = 0; i < MAXACCVALUES - 1; i++) { 
            vals[i] = vals[i + 1]; 
            } 
            count --; 
        } 
        vals[count++]=val; 
    } 
 
    float stddev()  
    { 
        if (!hasData()) return -1; 
        int u  =avg(); 
        int t = 0; 
        for (int i=0;ivals[j]) { 
                    float t = vals[j]; 
                    vals[j]=vals[i]; 
                    vals[i]=t; 
                } 
  
            } 
        } 
        return vals[count/2]; 
    } 
 
    float avg()  
    { 
        if (!hasData()) return -1; 
        float t = 0; 
        for (int i=0;ivals[i]) { 
                t=vals[i]; 
            } 
        } 
        return t; 
    } 
 
    float vmax()  
    { 
        if (!hasData()) return -1; 
        float t = vals[0]; 
        for (int i=0;it) { 
                t=vals[i]; 
            } 
        } 
        return t; 
    } 
};

本網站採用的污染指數和顏色與EPA是完全相同的。 EPA的指數可以從 AirNow上查到

空气质量指数	空气质量指数级别（状况）及表示颜色	对健康影响情况	建议采取的措施
0 - 50	一级（优）	空气质量令人满意，基本无空气污染	各类人群可正常活动
51 -100	二级（良）	空气质量可接受，但某些污染物可能对极少数异常敏感人群健康有较弱影响	极少数异常敏感人群应减少户外活动
101-150	三级（轻度污染）	易感人群症状有轻度加剧，健康人群出现刺激症状	儿童、老年人及心脏病、呼吸系统疾病患者应减少长时间、高强度的户外锻炼
151-200	四级（中度污染）	进一步加剧易感人群症状，可能对健康人群心脏、呼吸系统有影响	儿童、老年人及心脏病、呼吸系统疾病患者避免长时间、高强度的户外锻炼，一般人群适量减少户外运动
201-300	五级（重度污染）	心脏病和肺病患者症状显著加剧，运动耐受力降低，健康人群普遍出现症状	儿童、老年人及心脏病、肺病患者应停留在室内，停止户外运动，一般人群减少户外运动
300+	六级（严重污染）	健康人群运动耐受力降低，有明显强烈症状，提前出现某些疾病	儿童、老年人和病人应停留在室内，避免体力消耗，一般人群避免户外活动

(参考详见 http://zh.wikipedia.org/wiki/空气质量指数)

如果你想了解更多有關空氣質量與污染，詳見維基百科或者 AirNow

有關健康建議詳​​見北京的Richard Saint Cyr MD醫生的博客：www.myhealthbeijing.com